class_docstr: HGN sets feature gating and instance gating to get the important feature and item for predicting the next item
code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding):
          normal_(module.weight.data, 0.0, 1 / self.embedding_size)
      elif isinstance(module, nn.Linear):
          xavier_uniform_(module.weight.data)
          if module.bias is not None:
              constant_(module.bias.data, 0)
  
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 3 for calling the HGN obj's underlying member method code with example usage: obj._init_weights(module) without return types