code: def parametric_mean_function(max_iters=100, optimize=True, plot=True):
      
      #create  simple mean function
      mf = GPy.core.Mapping(1,1)
      mf.f = np.sin
  
      X = np.linspace(0,10,50).reshape(-1,1)
      Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape) + 3*X
  
      mf = GPy.mappings.Linear(1,1)
  
      k =GPy.kern.RBF(1)
      lik = GPy.likelihoods.Gaussian()
      m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
      if optimize:
          m.optimize(max_iters=max_iters)
      if plot:
          m.plot()
      return m
docs: A linear mean function with parameters that we'll learn alongside the kernel