class_docstr: LINE is a graph embedding model.
  
  We implement the model to train users and items embedding for recommendation.
code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
  
      # get user embedding from storage variable
      u_embeddings = self.user_embedding(user)
      i_embedding = self.item_embedding.weight
      # dot with all item embedding to accelerate
      scores = torch.matmul(u_embeddings, i_embedding.transpose(0, 1))
  
      return scores.view(-1)
  
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 10 for calling the LINE obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores