class_docstr: FISM is an item-based model for generating top-N recommendations that learns the
  item-item similarity matrix as the product of two low dimensional latent factor matrices.
  These matrices are learned using a structural equation modeling approach, where in the
  value being estimated is not used for its own estimation.
class_docstr_tok: ["FISM","is","an","item-based","model","for","generating","top-N","recommendations","that","learns","the","item-item","similarity","matrix","as","the","product","of","two","low","dimensional","latent","factor","matrices",".","These","matrices","are","learned","using","a","structural","equation","modeling","approach",",","where","in","the","value","being","estimated","is","not","used","for","its","own","estimation","."]
docstr: Initialize the module's parameters
  
  Note:
      It's a little different from the source code, because pytorch has no function to initialize
      the parameters by truncated normal distribution, so we replace it with xavier normal distribution
docstr_tok: ["Initialize","the","module","'s","parameters","Note",":","It","'s","a","little","different","from","the","source","code",",","because","pytorch","has","no","function","to","initialize","the","parameters","by","truncated","normal","distribution",",","so","we","replace","it","with","xavier","normal","distribution"]
code: def _init_weights(self, module):
      """Initialize the module's parameters
  
      Note:
          It's a little different from the source code, because pytorch has no function to initialize
          the parameters by truncated normal distribution, so we replace it with xavier normal distribution
  
      """
      if isinstance(module, nn.Embedding):
          normal_(module.weight.data, 0, 0.01)
  
file_path: RecBole/recbole/model/general_recommender/fism.py
imports: import torch
  import torch.nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.utils import InputType
label_desc: Use image node_id 4 for calling the FISM obj's underlying member method code with example usage: obj._init_weights(module) without return types