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 predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
  
      user_all_embeddings, item_all_embeddings = self.forward()
  
      u_embeddings = user_all_embeddings[user]
      i_embeddings = item_all_embeddings[item]
      scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1)
      return scores
  
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 6 for calling the LightGCN obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores