code: def pyro_model(self, input, beta=1.0, name_prefix=""):
      # Inducing values p(u)
      with pyro.poutine.scale(scale=beta):
          prior_distribution = (
              self.variational_strategy.prior_distribution
          )
          prior_distribution = prior_distribution.to_event(
              len(prior_distribution.batch_shape)
          )
          u_samples = pyro.sample(
              name_prefix + ".u", prior_distribution
          )
  
      # Include term for GPyTorch priors
      log_prior = torch.tensor(
          0.0, dtype=u_samples.dtype, device=u_samples.device
      )
      for _, module, prior, closure, _ in self.named_priors():
          log_prior.add_(prior.log_prob(closure(module)).sum())
      pyro.factor(name_prefix + ".log_prior", log_prior)
  
      # Include factor for added loss terms
      added_loss = torch.tensor(
          0.0, dtype=u_samples.dtype, device=u_samples.device
      )
      for added_loss_term in self.added_loss_terms():
          added_loss.add_(added_loss_term.loss())
      pyro.factor(name_prefix + ".added_loss", added_loss)
  
      # Draw samples from p(f)
      function_dist = self(input, prior=True)
      function_dist = pyro.distributions.Normal(
          loc=function_dist.mean, scale=function_dist.stddev
      ).to_event(len(function_dist.event_shape) - 1)
      return function_dist.mask(False)
  
imports: import pyro
  import torch
label_desc: Use image node_id 2 for calling the _PyroMixin obj's underlying member method code with example usage: obj.pyro_model(input, beta, name_prefix) and returns: function_dist