code: def forward(self, x, z):
      post_mu, post_logvar = self.encoder_old(x, 0)
  
      stnd_prior = log_norm_pdf(z, self.mu_prior, self.logvar_prior)
      post_prior = log_norm_pdf(z, post_mu, post_logvar)
      unif_prior = log_norm_pdf(
          z, self.mu_prior, self.logvar_uniform_prior
      )
  
      gaussians = [stnd_prior, post_prior, unif_prior]
      gaussians = [
          g.add(np.log(w))
          for g, w in zip(gaussians, self.mixture_weights)
      ]
  
      density_per_gaussian = torch.stack(gaussians, dim=-1)
  
      return torch.logsumexp(density_per_gaussian, dim=-1)
  
imports: import numpy
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import GeneralRecommender, AutoEncoderMixin
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the CompositePrior obj's underlying member method code with example usage: obj.forward(x, z) and returns: torch