code: def test_acquisition_per_second(acq_func):
      X = np.reshape(np.linspace(4.0, 8.0, 10), (-1, 1))
      y = np.vstack((np.ones(10), np.ravel(np.log(X)))).T
      cgpr = ConstantGPRSurrogate(Space(((1.0, 9.0),)))
      cgpr.fit(X, y)
  
      X_pred = np.reshape(np.linspace(1.0, 11.0, 20), (-1, 1))
      indices = np.arange(6)
      vals = _gaussian_acquisition(
          X_pred, cgpr, y_opt=1.0, acq_func=acq_func
      )
      for fast, slow in zip(indices[:-1], indices[1:]):
          assert vals[slow] > vals[fast]
  
      acq_wo_time = _gaussian_acquisition(
          X, cgpr.estimators_[0], y_opt=1.2, acq_func=acq_func[:2]
      )
      acq_with_time = _gaussian_acquisition(
          X, cgpr, y_opt=1.2, acq_func=acq_func
      )
      assert_array_almost_equal(
          acq_wo_time / acq_with_time, np.ravel(X), 2
      )
  
imports: import numpy
  import pytest
  from scipy import optimize
  from sklearn.multioutput import MultiOutputRegressor
  from numpy.testing import assert_array_almost_equal
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_raises
  from skopt.acquisition import _gaussian_acquisition
  from skopt.acquisition import gaussian_acquisition_1D
  from skopt.acquisition import gaussian_ei
  from skopt.acquisition import gaussian_lcb
  from skopt.acquisition import gaussian_pi
  from skopt.learning import GaussianProcessRegressor
  from skopt.learning.gaussian_process.kernels import Matern
  from skopt.learning.gaussian_process.kernels import WhiteKernel
  from skopt.space import Space
  from skopt.utils import cook_estimator
label_desc: Use image node_id 9 for calling a global function with example usage: test_acquisition_per_second(acq_func) without return types