class_docstr: GRU4Rec is a model that incorporate RNN for recommendation.
  
  Note:
  
      Regarding the innovation of this article,we can only achieve the data augmentation mentioned
      in the paper and directly output the embedding of the item,
      in order that the generation method we used is common to other sequential models.
class_docstr_tok: ["GRU4Rec","is","a","model","that","incorporate","RNN","for","recommendation",".","Note",":","Regarding","the","innovation","of","this","article",",","we","can","only","achieve","the","data","augmentation","mentioned","in","the","paper","and","directly","output","the","embedding","of","the","item",",","in","order","that","the","generation","method","we","used","is","common","to","other","sequential","models","."]
code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding):
          xavier_normal_(module.weight)
      elif isinstance(module, nn.GRU):
          xavier_uniform_(module.weight_hh_l0)
          xavier_uniform_(module.weight_ih_l0)
  
file_path: RecBole/recbole/model/sequential_recommender/gru4rec.py
imports: import torch
  from torch import nn
  from torch.nn.init import xavier_uniform_, xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 2 for calling the GRU4Rec obj's underlying member method code with example usage: obj._init_weights(module) without return types