code: def test_identical_regressors():
      newX = X.copy()
      newX[:, 1] = newX[:, 0]
      gamma = np.zeros(n_features)
      gamma[0] = gamma[1] = 1.0
      newy = np.dot(newX, gamma)
      warning_message = (
          "Orthogonal matching pursuit ended prematurely "
          "due to linear dependence in the dictionary. "
          "The requested precision might not have been met."
      )
      with pytest.warns(RuntimeWarning, match=warning_message):
          orthogonal_mp(newX, newy, n_nonzero_coefs=2)
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_sparse_coded_signal
  from sklearn.linear_model import LinearRegression, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, orthogonal_mp, orthogonal_mp_gram
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal, ignore_warnings
label_desc: Use image node_id 12 for calling a global function with example usage: test_identical_regressors() without return types