class_docstr: Collaborative Denoising Auto-Encoder (RecVAE) is a recommendation model
  for top-N recommendation with implicit feedback.
  
  We implement the model following the original author
class_docstr_tok: ["Collaborative","Denoising","Auto-Encoder","(","RecVAE",")","is","a","recommendation","model","for","top-N","recommendation","with","implicit","feedback",".","We","implement","the","model","following","the","original","author"]
code: def predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
  
      rating_matrix = self.get_rating_matrix(user)
  
      scores, _, _, _ = self.forward(rating_matrix, self.dropout_prob)
  
      return scores[[torch.arange(len(item)).to(self.device), item]]
  
imports: import numpy
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import GeneralRecommender, AutoEncoderMixin
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the RecVAE obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores