class_docstr: RaCT is a collaborative filtering model which uses methods based on actor-critic reinforcement learning for training.
  
  We implement the RaCT model with only user dataloader.
class_docstr_tok: ["RaCT","is","a","collaborative","filtering","model","which","uses","methods","based","on","actor-critic","reinforcement","learning","for","training",".","We","implement","the","RaCT","model","with","only","user","dataloader","."]
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)
  
      return scores[[torch.arange(len(item)).to(self.device), item]]
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  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 13 for calling the RaCT obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores