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 full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
  
      rating_matrix = self.get_rating_matrix(user)
  
      scores, _, _ = self.forward(rating_matrix)
  
      return scores.view(-1)
  
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 8 for calling the MacridVAE obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores