class_docstr: MacridVAE is an item-based collaborative filtering model that learns disentangled representations from user
  behavior and simultaneously ranks all items for each user.
  
  We implement the model following the original author.
class_docstr_tok: ["MacridVAE","is","an","item-based","collaborative","filtering","model","that","learns","disentangled","representations","from","user","behavior","and","simultaneously","ranks","all","items","for","each","user",".","We","implement","the","model","following","the","original","author","."]
code: def calculate_loss(self, interaction):
      user = interaction[self.USER_ID]
  
      rating_matrix = self.get_rating_matrix(user)
  
      self.update += 1
      if self.total_anneal_steps > 0:
          anneal = min(
              self.anneal_cap,
              1.0 * self.update / self.total_anneal_steps,
          )
      else:
          anneal = self.anneal_cap
  
      z, mu, logvar = self.forward(rating_matrix)
      kl_loss = None
      for i in range(self.kfac):
          kl_ = -0.5 * torch.mean(
              torch.sum(1 + logvar[i] - logvar[i].exp(), dim=1)
          )
          kl_loss = kl_ if (kl_loss is None) else (kl_loss + kl_)
  
      # CE loss
      ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean()
  
      if self.regs[0] != 0 or self.regs[1] != 0:
          return ce_loss + kl_loss * anneal + self.reg_loss()
  
      return ce_loss + kl_loss * anneal
  
file_path: RecBole/recbole/model/general_recommender/macridvae.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.model.loss import EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the MacridVAE obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: unknown, unknown