code: def model(self, data, batch_size, **kwargs):
      pyro.module("decode", self.decode)
      with pyro.plate("data", data.size(0), device=data.device) as ix:
          batch = data[ix]
          n = batch.size(0)
          (z_where, z_pres), x = self.prior(n, **kwargs)
          pyro.sample(
              "obs",
              dist.Normal(
                  x.view(n, -1),
                  (
                      self.likelihood_sd
                      * torch.ones(n, self.x_size**2, **self.options)
                  ),
              ).to_event(1),
              obs=batch.view(n, -1),
          )
  
imports: from collections import namedtuple
  import torch
  import torch.nn
  import torch.nn.functional
  from modules import MLP, Decoder, Encoder, Identity, Predict
  import pyro
  import pyro.distributions
label_desc: Use image node_id 4 for calling the AIR obj's underlying member method code with example usage: obj.model(data, batch_size) without return types