class_docstr: DSSM respectively expresses user and item as low dimensional vectors with mlp layers,
  and uses cosine distance to calculate the distance between the two semantic vectors.
code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding):
          xavier_normal_(module.weight.data)
      elif isinstance(module, nn.Linear):
          xavier_normal_(module.weight.data)
          if module.bias is not None:
              constant_(module.bias.data, 0)
  
file_path: RecBole/recbole/model/context_aware_recommender/dssm.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.layers import MLPLayers
label_desc: Use image node_id 2 for calling the DSSM obj's underlying member method code with example usage: obj._init_weights(module) without return types