class_docstr: LightGCN is a GCN-based recommender model.
  
  LightGCN includes only the most essential component in GCN — neighborhood aggregation — for
  collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly
  propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings
  learned at all layers as the final embedding.
  
  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]
  
      # calculate BPR Loss
      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)
  
      # calculate regularization Loss
      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,
          require_pow=self.require_pow,
      )
  
      loss = mf_loss + self.reg_weight * reg_loss
  
      return loss
  
file_path: RecBole/recbole/model/general_recommender/lightgcn.py
imports: import numpy
  import scipy.sparse
  import torch
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the LightGCN obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss