class_docstr: xDeepFM combines a CIN (Compressed Interaction Network) with a classical DNN.
  The model is able to learn certain bounded-degree feature interactions explicitly;
  Besides, it can also learn arbitrary low- and high-order feature interactions implicitly.
class_docstr_tok: ["xDeepFM","combines","a","CIN","(","Compressed","Interaction","Network",")","with","a","classical","DNN",".","The","model","is","able","to","learn","certain","bounded-degree","feature","interactions","explicitly",";","Besides",",","it","can","also","learn","arbitrary","low-","and","high-order","feature","interactions","implicitly","."]
code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding) or isinstance(
          module, nn.Conv1d
      ):
          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/xdeepfm.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, activation_layer
label_desc: Use image node_id 2 for calling the xDeepFM obj's underlying member method code with example usage: obj._init_weights(module) without return types