code: def forward(self, x, adj):
      mu, logvar = self.encode(x, adj)
      z = self.reparameterize(mu, logvar)
      if self.prediction_type == "stacked_ele_prod":
          link_logits = self.dc([z])
          recovered = self.dc([mu])
      else:
          link_logits = self.dc(z)
          recovered = self.dc(mu)
      return self.act(link_logits), mu, logvar, recovered
  
file_path: graph4nlp/graph4nlp/pytorch/test/link_prediction/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from layers import GraphConvolution
  from ...modules.prediction.classification.link_prediction.ConcatFeedForwardNNLayer import ConcatFeedForwardNNLayer
  from ...modules.prediction.classification.link_prediction.ElementSumLayer import ElementSumLayer
  from ...modules.prediction.classification.link_prediction.StackedElementProdLayer import StackedElementProdLayer
label_desc: Use image node_id 4 for calling the GCNModelVAE obj's underlying member method code with example usage: obj.forward(x, adj) and returns: self,  mu,  logvar,  recovered