class_docstr: DGCF is a disentangled representation enhanced matrix factorization model.
  The interaction matrix of :math:`n_{users} \times n_{items}` is decomposed to :math:`n_{factors}` intent graph,
  we carefully design the data interface and use sparse tensor to train and test efficiently.
  We implement the model following the original author with a pairwise training mode.
code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
      if self.restore_user_e is None or self.restore_item_e is None:
          self.restore_user_e, self.restore_item_e = self.forward()
      u_embeddings = self.restore_user_e[user]
  
      scores = torch.matmul(
          u_embeddings, self.restore_item_e.transpose(0, 1)
      )
  
      return scores.view(-1)
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 10 for calling the DGCF obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores