class_docstr: MultiDAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.
  
  We implement the the MultiDAE model with only user dataloader.
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/multidae.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.layers import MLPLayers
  from recbole.utils import InputType
label_desc: Use image node_id 6 for calling the MultiDAE obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores