code: def test_gpr_lml_error():
      """Check that we raise the proper error in the LML method."""
      gpr = GaussianProcessRegressor(kernel=RBF()).fit(X, y)
  
      err_msg = "Gradient can only be evaluated for theta!=None"
      with pytest.raises(ValueError, match=err_msg):
          gpr.log_marginal_likelihood(eval_gradient=True)
  
imports: import re
  import sys
  import warnings
  import numpy
  import pytest
  from scipy.optimize import approx_fprime
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import RBF, DotProduct, ExpSineSquared, WhiteKernel
  from sklearn.gaussian_process.kernels import ConstantKernel
  from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_less
label_desc: Use image node_id 28 for calling a global function with example usage: test_gpr_lml_error() without return types