code: def test_gpr_correct_error_message():
      X = np.arange(12).reshape(6, -1)
      y = np.ones(6)
      kernel = DotProduct()
      gpr = GaussianProcessRegressor(kernel=kernel, alpha=0.0)
      message = (
          "The kernel, %s, is not returning a "
          "positive definite matrix. Try gradually increasing "
          "the 'alpha' parameter of your "
          "GaussianProcessRegressor estimator." % kernel
      )
      with pytest.raises(
          np.linalg.LinAlgError, match=re.escape(message)
      ):
          gpr.fit(X, y)
  
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 20 for calling a global function with example usage: test_gpr_correct_error_message() without return types