class_docstr: STAMP is capable of capturing users’ general interests from the long-term memory of a session context,
  whilst taking into account users’ current interests from the short-term memory of the last-clicks.
  
  
  Note:
  
      According to the test results, we made a little modification to the score function mentioned in the paper,
      and did not use the final sigmoid activation function.
class_docstr_tok: ["STAMP","is","capable","of","capturing","users","\u2019","general","interests","from","the","long-term","memory","of","a","session","context",",","whilst","taking","into","account","users","\u2019","current","interests","from","the","short-term","memory","of","the","last-clicks",".","Note",":","According","to","the","test","results",",","we","made","a","little","modification","to","the","score","function","mentioned","in","the","paper",",","and","did","not","use","the","final","sigmoid","activation","function","."]
code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding):
          normal_(module.weight.data, 0, 0.002)
      elif isinstance(module, nn.Linear):
          normal_(module.weight.data, 0, 0.05)
          if module.bias is not None:
              module.bias.data.fill_(0.0)
  
file_path: RecBole/recbole/model/sequential_recommender/stamp.py
imports: import torch
  from torch import nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 2 for calling the STAMP obj's underlying member method code with example usage: obj._init_weights(module) without return types