code: def test_distance_string():
      # Inspired by test_hamming_string_array in scipy.tests.test_distance
      a = np.array(
          [
              "eggs",
              "spam",
              "spam",
              "eggs",
              "spam",
              "spam",
              "spam",
              "spam",
              "spam",
              "spam",
              "spam",
              "eggs",
              "eggs",
              "spam",
              "eggs",
              "eggs",
              "eggs",
              "eggs",
              "eggs",
              "spam",
          ],
          dtype="|S4",
      )
      b = np.array(
          [
              "eggs",
              "spam",
              "spam",
              "eggs",
              "eggs",
              "spam",
              "spam",
              "spam",
              "spam",
              "eggs",
              "spam",
              "eggs",
              "spam",
              "eggs",
              "spam",
              "spam",
              "eggs",
              "spam",
              "spam",
              "eggs",
          ],
          dtype="|S4",
      )
      true_values = np.array([[0, 0.45], [0.45, 0]])
      X = np.vstack((a, b))
      hm = HammingKernel()
      assert_array_almost_equal(-np.log(hm(X)) / 20.0, true_values)
  
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 7 for calling a global function with example usage: test_distance_string() without return types