class_docstr: Collaborative Denoising Auto-Encoder (CDAE) is a recommendation model
  for top-N recommendation that utilizes the idea of Denoising Auto-Encoders.
  We implement the the CDAE model with only user dataloader.
class_docstr_tok: ["Collaborative","Denoising","Auto-Encoder","(","CDAE",")","is","a","recommendation","model","for","top-N","recommendation","that","utilizes","the","idea","of","Denoising","Auto-Encoders",".","We","implement","the","the","CDAE","model","with","only","user","dataloader","."]
code: def full_sort_predict(self, interaction):
      users = interaction[self.USER_ID]
  
      items = self.get_rating_matrix(users)
      predict = self.forward(items, users)
      predict = self.o_act(predict)
      return predict.view(-1)
  
file_path: RecBole/recbole/model/general_recommender/cdae.py
imports: import torch
  import torch.nn
  from recbole.model.abstract_recommender import AutoEncoderMixin, GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the CDAE obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: predict