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 forward(self, rating_matrix):
      h = F.normalize(rating_matrix)
  
      h = F.dropout(h, self.drop_out, training=self.training)
  
      h = self.encoder(h)
      return self.decoder(h)
  
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 3 for calling the MultiDAE obj's underlying member method code with example usage: obj.forward(rating_matrix) and returns: self