text: Title: How to properly sample within `SVI`?
  Body: Hi, I think I'm severely misunderstanding or misusing `sample` within SVI. Here is (a skeleton of) some of the code.
  
  ```python
  
  def model():
          sample_shape = (100, )
  
          gamma_prior = dist.Normal(-20, 5).expand(sample_shape)
          gammas = numpyro.sample('gammas', gamma_prior)
  
          ### below, run simulation with parameters and compute loss with output
          . 
          .
          .
  
          numpyro.factor("loss", -total_loss)
  
      guide = AutoBNAFNormal(model, num_flows=1)
      optimizer = Adam(step_size=1e-2)
      svi = SVI(model, guide, optimizer, loss=Trace_ELBO())
      .
      .
      ### below is training loop
  ```
  
  What's expected is that `gammas` are drawn from a prior (Gaussian) distribution with the above parameters. However, whatever I do, these samples seem to only come from the standard Gaussian when printing, e.g. it is ignoring the mean and variance I have defined.
  
  If I draw parameters from the prior this way:
  
  ```python
  gamma_prior = dist.Normal(-20, 5).expand(sample_shape)
  gammas_samples = gamma_prior.sample(jax.random.PRNGKey(1))
  ```
  
  The behavior is as expected. What am I doing wrong?? 