How to check the final variational distribution after SVI optimization?

The guide() is the variational distribution, which approximates the posterior distribution. When the SVI optimization is done, how do we check the final ‘optimized’ variational distribution?

To be concrete, let’s say we have the following guide, where myNet is an instance of a neural network:

def guide(x_data, y_data):   
    for name, param in myNet.named_parameters():
        if param.requires_grad:
            mu = torch.randn_like(param)
            priors[f'{name}'] = Normal( loc=pyro.param(f'{name}_mu', torch.randn_like(param)), \
                                                  scale=softplus( pyro.param(f'{name}_sigma', torch.randn_like(param)) ) )
    lifted_module = pyro.random_module("module", myNet, priors)
    return lifted_module()

i dont know what “check” means. to get posterior predictives, you can follow this tutorial. to see the learned parameters, you can print the paramstore.