class_docstr: MultiVAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.
  
  We implement the MultiVAE model with only user dataloader.
code: def reparameterize(self, mu, logvar):
      if self.training:
          std = torch.exp(0.5 * logvar)
          epsilon = torch.zeros_like(std).normal_(mean=0, std=0.01)
          return mu + epsilon * std
      else:
          return mu
  
file_path: RecBole/recbole/model/general_recommender/multivae.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import AutoEncoderMixin, GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 3 for calling the MultiVAE obj's underlying member method code with example usage: obj.reparameterize(mu, logvar) and returns: unknown, mu