new_contents: import matplotlib.pyplot as plt
  import numpy as np
  import logging
  
  from robo.models.bnn import SGLDNet
  from robo.initial_design.init_random_uniform import  init_random_uniform
  
  
  def f(x):
      return np.sinc(x * 10 - 5).sum(axis=1)[:, None]
  
  logging.basicConfig(level=logging.INFO)
  
  rng = np.random.RandomState(42)
  
  X = init_random_uniform(np.zeros(1), np.ones(1), 50, rng)
  Y = f(X)
  
  model = SGLDNet(l_rate=1e-3, burn_in=30000, n_iters=100000)
  model.train(X, Y)
  
  x = np.linspace(0, 1, 100)[:, None]
  
  vals = f(x)
  
  mean_pred, var_pred = model.predict(x)
  
  std_pred = np.sqrt(var_pred)
  
  plt.grid()
  plt.plot(x[:, 0], mean_pred[:, 0], label="mean", color="blue")
  plt.plot(x[:, 0], vals[:, 0], label="true", color="green")
  plt.plot(X[:, 0], Y[:, 0], "ro")
  plt.fill_between(x[:, 0], mean_pred[:, 0] + std_pred[:, 0], mean_pred[:, 0] - std_pred[:, 0], alpha=0.2, color="orange")
  
  plt.legend()
  plt.show()
  
  
  
  
  
subject: add example for sgld net