class_docstr: LINE is a graph embedding model.
  
  We implement the model to train users and items embedding for recommendation.
code: def get_used_ids(self):
      cur = np.array([set() for _ in range(self.n_items)])
      for uid, iid in zip(
          self.interaction_feat[self.USER_ID].numpy(),
          self.interaction_feat[self.ITEM_ID].numpy(),
      ):
          cur[iid].add(uid)
      return cur
  
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 2 for calling the LINE obj's underlying member method code with example usage: obj.get_used_ids() and returns: cur