code: def test_gp_regressor():
      rng = np.random.RandomState(0)
      X = np.asarray(
          [
              ["ham", "spam", "ted"],
              ["ham", "ted", "ted"],
              ["ham", "spam", "spam"],
          ]
      )
      y = rng.randn(3)
      hm = HammingKernel(length_scale=[1.0, 1.0, 1.0])
      if UseOrdinalEncoder:
          enc = OrdinalEncoder()
          enc.fit(X)
  
      gpr = GaussianProcessRegressor(hm)
      if UseOrdinalEncoder:
          gpr.fit(enc.transform(X), y)
          assert_array_almost_equal(gpr.predict(enc.transform(X)), y)
          assert_array_almost_equal(
              gpr.predict(enc.transform(X[:2])), y[:2]
          )
      else:
          gpr.fit(X, y)
          assert_array_almost_equal(gpr.predict(X), y)
          assert_array_almost_equal(gpr.predict(X[:2]), y[:2])
  
imports: import numpy
  from scipy import optimize
  from scipy.spatial.distance import pdist, squareform
  from numpy.testing import assert_array_almost_equal
  from numpy.testing import assert_array_equal
  import pytest
  from skopt.learning.gaussian_process import GaussianProcessRegressor
  from skopt.learning.gaussian_process.kernels import ConstantKernel
  from skopt.learning.gaussian_process.kernels import DotProduct
  from skopt.learning.gaussian_process.kernels import ExpSineSquared
  from skopt.learning.gaussian_process.kernels import HammingKernel
  from skopt.learning.gaussian_process.kernels import Matern
  from skopt.learning.gaussian_process.kernels import RationalQuadratic
  from skopt.learning.gaussian_process.kernels import RBF
  from skopt.learning.gaussian_process.kernels import WhiteKernel
label_desc: Use image node_id 12 for calling a global function with example usage: test_gp_regressor() without return types