class_docstr: Collaborative Denoising Auto-Encoder (RecVAE) is a recommendation model
  for top-N recommendation with implicit feedback.
  
  We implement the model following the original author
class_docstr_tok: ["Collaborative","Denoising","Auto-Encoder","(","RecVAE",")","is","a","recommendation","model","for","top-N","recommendation","with","implicit","feedback",".","We","implement","the","model","following","the","original","author"]
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
  
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 RecVAE obj's underlying member method code with example usage: obj.reparameterize(mu, logvar) and returns: unknown, mu