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 critic_forward(self, actor_loss):
      h = self.construct_critic_input(actor_loss)
      y = self.critic_net(h)
      y = torch.squeeze(y)
      return y
  
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 9 for calling the RaCT obj's underlying member method code with example usage: obj.critic_forward(actor_loss) and returns: y