code: def test_bound_check_fixed_hyperparameter():
      # Regression test for issue #17943
      # Check that having a hyperparameter with fixed bounds doesn't cause an
      # error
      k1 = 50.0**2 * RBF(
          length_scale=50.0
      )  # long term smooth rising trend
      k2 = ExpSineSquared(
          length_scale=1.0, periodicity=1.0, periodicity_bounds="fixed"
      )  # seasonal component
      kernel = k1 + k2
      GaussianProcessRegressor(kernel=kernel).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 24 for calling a global function with example usage: test_bound_check_fixed_hyperparameter() without return types