code: def reparameterize(self, mu, logvar):
      if self.training:
          std = torch.exp(0.5 * logvar)
          eps = torch.zeros_like(std).normal_(mean=0, std=0.01)
          return mu + eps * std
      else:
          return mu
  
imports: import numpy
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the VAE obj's underlying member method code with example usage: obj.reparameterize(mu, logvar) and returns: unknown, mu