code: def test_anisotropic_kernel():
      rng = np.random.RandomState(0)
      X = rng.randint(0, 4, (5, 3))
      hm = HammingKernel()
      X_kernel = hm(X)
      hm_aniso = HammingKernel(length_scale=[1.0, 1.0, 1.0])
      X_kernel_aniso = hm_aniso(X)
      assert_array_almost_equal(X_kernel, X_kernel_aniso)
  
      hm = HammingKernel(length_scale=2.0)
      X_kernel = hm(X)
      hm_aniso = HammingKernel(length_scale=[2.0, 2.0, 2.0])
      X_kernel_aniso = hm_aniso(X)
      assert_array_almost_equal(X_kernel, X_kernel_aniso)
  
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 9 for calling a global function with example usage: test_anisotropic_kernel() without return types