old_contents: import tensorprob as tp
  
  model = tp.Model()
  
  with model:
      mu = tp.scalar('mu')
      f_normal = tp.scalar('f_normal', lower=0, upper=1)
      sigma1 = tp.scalar('sigma1', lower=0)
      sigma2 = tp.scalar('sigma2', lower=0)
      lamb = tp.scalar('lambda')
      f = tp.scalar('f', lower=0, upper=1)
  
      X1 = tp.Normal2(mu, f_normal, sigma1, sigma2)
      X2 = tp.Exponential(lamb)
      X_ = tp.Mix2(f, X1, X2)
  
      X = tp.Bound(X_, 100, 200)
  
  inits = {mu: 150, sigma1: 10, sigma2: 20, f_normal: 0.5, lamb: 0.01, f: 0.2, }
  
  model.fit(X=[1, 2, 3], inits=inits)
  
new_contents: import tensorprob as tp
  
  model = tp.Model()
  
  with model:
      mu = tp.Scalar('mu')
      f_normal = tp.Scalar('f_normal', lower=0, upper=1)
      sigma1 = tp.Scalar('sigma1', lower=0)
      sigma2 = tp.Scalar('sigma2', lower=0)
      lamb = tp.Scalar('lambda')
      f = tp.Scalar('f', lower=0, upper=1)
  
      X1 = tp.Normal2(mu, f_normal, sigma1, sigma2)
      X2 = tp.Exponential(lamb)
      X_ = tp.Mix2(f, X1, X2)
  
      X = tp.Bound(X_, 100, 200)
  
  inits = {mu: 150, sigma1: 10, sigma2: 20, f_normal: 0.5, lamb: 0.01, f: 0.2, }
  
  model.fit(X=[1, 2, 3], inits=inits)
  
subject: Change the example to account for Scalar being a class