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 construct_critic_input(self, actor_loss):
      critic_inputs = []
      critic_inputs.append(self.number_of_seen_items)
      critic_inputs.append(self.number_of_unseen_items)
      critic_inputs.append(actor_loss)
      return torch.stack(critic_inputs, dim=1)
  
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 6 for calling the RaCT obj's underlying member method code with example usage: obj.construct_critic_input(actor_loss) and returns: torch