class_docstr: FDSA is similar with the GRU4RecF implemented in RecBole, which uses two different Transformer encoders to
  encode items and features respectively and concatenates the two subparts' outputs as the final output.
class_docstr_tok: ["FDSA","is","similar","with","the","GRU4RecF","implemented","in","RecBole",",","which","uses","two","different","Transformer","encoders","to","encode","items","and","features","respectively","and","concatenates","the","two","subparts","'","outputs","as","the","final","output","."]
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/fdsa.py
imports: import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer, VanillaAttention
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 2 for calling the FDSA obj's underlying member method code with example usage: obj._init_weights(module) without return types