code: def test_noise_equals_gaussian():
      gpr1 = GaussianProcessRegressor(rbf + wk).fit(X, y)
  
      # gpr2 sets the noise component to zero at predict time.
      gpr2 = GaussianProcessRegressor(rbf, noise="gaussian").fit(X, y)
      assert not gpr1.noise_
      assert gpr2.noise_
      assert_almost_equal(gpr1.kernel_.k2.noise_level, gpr2.noise_, 4)
      mean1, std1 = gpr1.predict(X, return_std=True)
      mean2, std2 = gpr2.predict(X, return_std=True)
      assert_array_almost_equal(mean1, mean2, 4)
      assert not np.any(std1 == std2)
  
file_path: scikit-optimize/skopt/learning/gaussian_process/tests/test_gpr.py
imports: import numpy
  import pytest
  from scipy import optimize
  from numpy.testing import assert_almost_equal
  from numpy.testing import assert_array_almost_equal
  from numpy.testing import assert_array_equal
  from skopt.learning import GaussianProcessRegressor
  from skopt.learning.gaussian_process.kernels import RBF
  from skopt.learning.gaussian_process.kernels import Matern
  from skopt.learning.gaussian_process.kernels import WhiteKernel
  from skopt.learning.gaussian_process.gpr import _param_for_white_kernel_in_Sum
label_desc: Use image node_id 3 for calling a global function with example usage: test_noise_equals_gaussian() without return types