code: def test_solution_inside_bounds(kernel):
      # Test that hyperparameter-optimization remains in bounds#
      gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
  
      bounds = gpr.kernel_.bounds
      max_ = np.finfo(gpr.kernel_.theta.dtype).max
      tiny = 1e-10
      bounds[~np.isfinite(bounds[:, 1]), 1] = max_
  
      assert_array_less(bounds[:, 0], gpr.kernel_.theta + tiny)
      assert_array_less(gpr.kernel_.theta, bounds[:, 1] + tiny)
  
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 8 for calling a global function with example usage: test_solution_inside_bounds(kernel) without return types