Binomial Regression, pyro.get_param_store return point estimate

Hi! I am new to Pyro and I am trying to build a binomial regression model by modifying the introduction tutorial.
The model tries to fit n_trun ~ Binomial (total_count = n_read, p = y)
where y = Sigmoid(Linear(fr_gc, fr_u))

I used auto_guide to generate the guide function.

However, when I use pyro.get_param_store().items() to get the parameters, it does not have standard deviation, it return a point estimate

a 6.6135216
b_gc 0.2669241
b_u 0.06167726

instead of those in the tutorial that has a loc and scales

AutoNormal.locs.a 2.213837
AutoNormal.scales.a 0.007959719
AutoNormal.locs.bA -0.25122392
AutoNormal.scales.bA 0.018179549
AutoNormal.locs.bR -0.02946356
AutoNormal.scales.bR 0.0051593184
AutoNormal.locs.bAR 0.039513554
AutoNormal.scales.bAR 0.009952072
AutoNormal.locs.sigma -4.4010673
AutoNormal.scales.sigma 0.05531578

also, using pyro,plate to sample, returns nothing.

I was struggling for days and cannot find any way that solves this. Please help. Thanks

Here is my code.

def model(fr_gc, fr_u, n_read, n_trun=None):
    a = pyro.param("a", dist.Normal(0., 10.)) # name, value (when initialize) to return a tensor, or a tensor
    b_gc = pyro.param("b_gc", dist.Normal(0., 1.))
    b_u = pyro.param("b_u", dist.Normal(0., 1.))

    prob = torch.special.expit(a + b_gc * fr_gc + b_u * fr_u)
#     sigma = pyro.sample("sigma", dist.Uniform(0., 10.))

    with pyro.plate("data", len(fr_gc)): # equivilant to for loops
        return pyro.sample("obs", dist.Binomial(total_count = n_read, 
                                                probs = prob 
                                                ), obs=n_trun)
#           return pyro.sample("obs", dist.Normal(prob, sigma), obs = n_trun)
    # generates observed data from latent variables using primitive functions


# These should be reset each training loop.
auto_guide = pyro.infer.autoguide.AutoNormal(model) #pre-defined distribution over each hidden parameter

adam = pyro.optim.Adam({"lr": 0.02})
elbo = pyro.infer.Trace_ELBO()
svi = pyro.infer.SVI(model, auto_guide, adam, elbo) # we gonna use adam (SGD) to optimize elbo (SVI=stochastic variational inference)

losses = []
for step in range(1000 if not smoke_test else 2):  # Consider running for more steps.
    loss = svi.step(fr_gc, fr_u, n_read, n_trun)
    if step % 100 == 0:"Elbo loss: {}".format(loss))

Instead of writing a = pyro.param("a", dist.Normal(0., 10.)), you should use the pyro.sample function. So write

a = pyro.sample("a", dist.Normal(0., 10.))

and do the same for the other parameters