code: def forward(
      self,
      user_ratings,
      beta=None,
      gamma=1,
      dropout_rate=0.5,
      calculate_loss=True,
  ):
      mu, logvar = self.encoder(user_ratings, dropout_rate=dropout_rate)
      z = self.reparameterize(mu, logvar)
      x_pred = self.decoder(z)
      if calculate_loss:
          if gamma:
              norm = user_ratings.sum(dim=-1)
              kl_weight = gamma * norm
          elif beta:
              kl_weight = beta
  
          mll = (
              (F.log_softmax(x_pred, dim=-1) * user_ratings)
              .sum(dim=-1)
              .mean()
          )
          kld = (
              (
                  log_norm_pdf(z, mu, logvar)
                  - self.prior(user_ratings, z)
              )
              .sum(dim=-1)
              .mul(kl_weight)
              .mean()
          )
          negative_elbo = -(mll - kld)
  
          return (mll, kld), negative_elbo
  
      else:
          return x_pred
  
imports: import numpy
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the VAE obj's underlying member method code with example usage: obj.forward(user_ratings, beta, gamma, dropout_rate, calculate_loss) and returns: mll,  kld,  negative_elbo, x_pred