class_docstr: HGN sets feature gating and instance gating to get the important feature and item for predicting the next item
code: def predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      test_item = interaction[self.ITEM_ID]
      user = interaction[self.USER_ID]
      seq_output = self.forward(item_seq, user)
      test_item_emb = self.item_embedding_for_prediction(test_item)
      scores = torch.mul(seq_output, test_item_emb).sum(dim=1)
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/hgn.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_uniform_, constant_, normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 8 for calling the HGN obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores