class_docstr: HRM can well capture both sequential behavior and users’ general taste by involving transaction and
  user representations in prediction.
  
  HRM user max- & average- pooling as a good helper.
code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding):
          xavier_normal_(module.weight.data)
  
file_path: RecBole/recbole/model/sequential_recommender/hrm.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 3 for calling the HRM obj's underlying member method code with example usage: obj._init_weights(module) without return types