class_docstr: This is an extension of SASRec, which concatenates item representations and item attribute representations
  as the input to the model.
class_docstr_tok: ["This","is","an","extension","of","SASRec",",","which","concatenates","item","representations","and","item","attribute","representations","as","the","input","to","the","model","."]
docstr: Initialize the weights
docstr_tok: ["Initialize","the","weights"]
code: def _init_weights(self, module):
      """Initialize the weights"""
      if isinstance(module, (nn.Linear, nn.Embedding)):
          # Slightly different from the TF version which uses truncated_normal for initialization
          # cf https://github.com/pytorch/pytorch/pull/5617
          module.weight.data.normal_(
              mean=0.0, std=self.initializer_range
          )
      elif isinstance(module, nn.LayerNorm):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0)
      if isinstance(module, nn.Linear) and module.bias is not None:
          module.bias.data.zero_()
  
file_path: RecBole/recbole/model/sequential_recommender/sasrecf.py
imports: import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer
  from recbole.model.loss import BPRLoss
  from recbole.utils import FeatureType
label_desc: Use image node_id 2 for calling the SASRecF obj's underlying member method code with example usage: obj._init_weights(module) without return types