class_docstr: LINE is a graph embedding model.
  
  We implement the model to train users and items embedding for recommendation.
code: def calculate_loss(self, interaction):
      user = interaction[self.USER_ID]
      pos_item = interaction[self.ITEM_ID]
      neg_item = interaction[self.NEG_ITEM_ID]
  
      score_pos = self.forward(user, pos_item)
  
      ones = torch.ones(len(score_pos), device=self.device)
  
      if self.order == 1:
          if random.random() < 0.5:
              score_neg = self.forward(user, neg_item)
          else:
              neg_user = self.sampler(pos_item)
              score_neg = self.forward(neg_user, pos_item)
          return self.loss_fct(ones, score_pos) + self.loss_fct(
              -1 * ones, score_neg
          )
  
      else:
          # randomly train i-i relation and u-u relation with u-i relation
          if random.random() < 0.5:
              score_neg = self.forward(user, neg_item)
              score_pos_con = self.context_forward(user, pos_item, "uu")
              score_neg_con = self.context_forward(user, neg_item, "uu")
          else:
              # sample negative user for item
              neg_user = self.sampler(pos_item)
              score_neg = self.forward(neg_user, pos_item)
              score_pos_con = self.context_forward(pos_item, user, "ii")
              score_neg_con = self.context_forward(
                  pos_item, neg_user, "ii"
              )
  
          return (
              self.loss_fct(ones, score_pos)
              + self.loss_fct(-1 * ones, score_neg)
              + self.loss_fct(ones, score_pos_con)
              * self.second_order_loss_weight
              + self.loss_fct(-1 * ones, score_neg_con)
              * self.second_order_loss_weight
          )
  
imports: import random
  import numpy
  import torch
  import torch.nn
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 8 for calling the LINE obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: unknown, unknown