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 calculate_loss(self, interaction):
      # clear the storage variable when training
      if (
          self.restore_user_e is not None
          or self.restore_item_e is not None
      ):
          self.restore_user_e, self.restore_item_e = None, None
  
      user = interaction[self.USER_ID]
      pos_item = interaction[self.ITEM_ID]
      neg_item = interaction[self.NEG_ITEM_ID]
  
      user_all_embeddings, item_all_embeddings = self.forward()
      u_embeddings = user_all_embeddings[user]
      pos_embeddings = item_all_embeddings[pos_item]
      neg_embeddings = item_all_embeddings[neg_item]
  
      pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1)
      neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1)
      mf_loss = self.mf_loss(pos_scores, neg_scores)
  
      # cul regularized
      u_ego_embeddings = self.user_embedding(user)
      pos_ego_embeddings = self.item_embedding(pos_item)
      neg_ego_embeddings = self.item_embedding(neg_item)
      reg_loss = self.reg_loss(
          u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings
      )
  
      if self.n_factors > 1 and self.cor_weight > 1e-9:
          cor_users, cor_items = sample_cor_samples(
              self.n_users, self.n_items, self.cor_batch_size
          )
          cor_users = torch.LongTensor(cor_users).to(self.device)
          cor_items = torch.LongTensor(cor_items).to(self.device)
          cor_u_embeddings = user_all_embeddings[cor_users]
          cor_i_embeddings = item_all_embeddings[cor_items]
          cor_loss = self.create_cor_loss(
              cor_u_embeddings, cor_i_embeddings
          )
          loss = (
              mf_loss
              + self.reg_weight * reg_loss
              + self.cor_weight * cor_loss
          )
      else:
          loss = mf_loss + self.reg_weight * reg_loss
      return loss
  
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 6 for calling the DGCF obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss