class_docstr: FiGNN is a CTR prediction model based on GGNN,
  which can model sophisticated interactions among feature fields on the graph-structured features.
class_docstr_tok: ["FiGNN","is","a","CTR","prediction","model","based","on","GGNN",",","which","can","model","sophisticated","interactions","among","feature","fields","on","the","graph-structured","features","."]
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)
      elif isinstance(module, nn.GRU):
          xavier_uniform_(module.weight_hh_l0)
          xavier_uniform_(module.weight_ih_l0)
  
file_path: RecBole/recbole/model/context_aware_recommender/fignn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_uniform_, xavier_normal_, constant_
  from itertools import product
  from recbole.utils import InputType
  from recbole.model.abstract_recommender import ContextRecommender
label_desc: Use image node_id 3 for calling the FiGNN obj's underlying member method code with example usage: obj._init_weights(module) without return types