code: def get_globals():
      X = np.array(
          [
              [0.00, 0.00],
              [0.99, 0.99],
              [0.00, 0.99],
              [0.99, 0.00],
              [0.50, 0.50],
              [0.25, 0.50],
              [0.50, 0.25],
              [0.75, 0.50],
              [0.50, 0.75],
          ]
      )
  
      def get_y(X):
          return (
              -((X[:, 0] - 0.3) ** 2) - 0.5 * (X[:, 1] - 0.6) ** 2 + 2
          )
  
      y = get_y(X)
  
      mesh = np.dstack(
          np.meshgrid(np.arange(0, 1, 0.005), np.arange(0, 1, 0.005))
      ).reshape(-1, 2)
  
      GP = GaussianProcessRegressor(
          kernel=Matern(),
          n_restarts_optimizer=25,
      )
      GP.fit(X, y)
  
      return {"x": X, "y": y, "gp": GP, "mesh": mesh}
  
file_path: BayesianOptimization/tests/test_util.py
imports: import pytest
  import numpy
  from bayes_opt import BayesianOptimization
  from bayes_opt.util import UtilityFunction, Colours
  from bayes_opt.util import acq_max, load_logs, ensure_rng
  from sklearn.gaussian_process.kernels import Matern
  from sklearn.gaussian_process import GaussianProcessRegressor
  from scipy.optimize import NonlinearConstraint
  from pathlib import Path
label_desc: Use image node_id 1 for calling a global function with example usage: get_globals() and returns: dict, unknown