code: def test_no_fit_default_predict():
      # Test that GPR predictions without fit does not break by default.
      default_kernel = C(1.0, constant_value_bounds="fixed") * RBF(
          1.0, length_scale_bounds="fixed"
      )
      gpr1 = GaussianProcessRegressor()
      _, y_std1 = gpr1.predict(X, return_std=True)
      _, y_cov1 = gpr1.predict(X, return_cov=True)
  
      gpr2 = GaussianProcessRegressor(kernel=default_kernel)
      _, y_std2 = gpr2.predict(X, return_std=True)
      _, y_cov2 = gpr2.predict(X, return_cov=True)
  
      assert_array_almost_equal(y_std1, y_std2)
      assert_array_almost_equal(y_cov1, y_cov2)
  
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 22 for calling a global function with example usage: test_no_fit_default_predict() without return types