text: import tensorflow as tf
  import numpy as np
  import ais
  import matplotlib.pyplot as plt
  from priors import NormalPrior
  from kernels import ParsenDensityEstimator
  from scipy.stats import norm
  
  
  class Generator(object):
      def __init__(self, input_dim, output_dim):
          self.input_dim = input_dim
          self.output_dim = output_dim
  
      def __call__(self, z):
          return z * 2 + 3
  
  
  generator = Generator(1, 1)
  prior = NormalPrior()
  kernel = ParsenDensityEstimator()
  model = ais.Model(generator, prior, kernel, 1000, 10000)
  
  #p = norm()
  #x = np.linspace(norm.ppf(0.01, loc=3, scale=2), norm.ppf(0.99, loc=3, scale=2), 100)
  #p1 = norm.pdf(x, loc=3, scale=2)
  #xx = np.reshape(x, [100, 1])
  
  num_samples = 1
  xx = np.reshape([2], [num_samples, 1])
  schedule = ais.get_schedule(num_samples, rad=4)
  
  p2 = model.ais(xx, schedule)
  
  print('results')
  print(p2)
  print(np.exp(p2))
  