code: def reparameterize(self, mu, logvar):
      if self.training:
          std = torch.exp(logvar)
          eps = torch.randn_like(std)
          return eps.mul(std).add_(mu)
      else:
          return mu
  
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 3 for calling the GCNModelVAE obj's underlying member method code with example usage: obj.reparameterize(mu, logvar) and returns: eps, mu