code: def target(x):
      return (
          np.exp(-((x - 2) ** 2))
          + np.exp(-((x - 6) ** 2) / 10)
          + 1 / (x**2 + 1)
      )
  
file_path: advisor/visualization/visualize_bayesian_optimization.py
imports: import numpy
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import Matern
  from scipy.stats import norm
  from scipy.optimize import minimize
  import matplotlib.pyplot
  from matplotlib import gridspec
label_desc: Use image node_id 1 for calling a global function with example usage: target(x) and returns: unknown