class_docstr: HGN sets feature gating and instance gating to get the important feature and item for predicting the next item
code: def forward(self, seq_item, user):
      seq_item_embedding = self.item_embedding(seq_item)
      user_embedding = self.user_embedding(user)
      feature_gating = self.feature_gating(
          seq_item_embedding, user_embedding
      )
      instance_gating = self.instance_gating(
          feature_gating, user_embedding
      )
      # batch_size * embedding_size
      item_item = torch.sum(seq_item_embedding, dim=1)
      # batch_size * embedding_size
  
      return user_embedding + instance_gating + item_item
  
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 6 for calling the HGN obj's underlying member method code with example usage: obj.forward(seq_item, user) and returns: unknown