code: def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
      
      optimizer='scg'
      x_len = 100
      X = np.linspace(0, 10, x_len)[:, None]
      f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.RBF(1).K(X))
      Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
  
      kern = GPy.kern.RBF(1)
      poisson_lik = GPy.likelihoods.Poisson()
      laplace_inf = GPy.inference.latent_function_inference.Laplace()
  
      # create simple GP Model
      m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf)
  
      if optimize:
          m.optimize(optimizer)
      if plot:
          m.plot()
          # plot the real underlying rate function
          pb.plot(X, np.exp(f_true), '--k', linewidth=2)
  
      return m
docs: Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.