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","."]
code: def predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
      output = torch.sigmoid(self.forward(user, item))
      return output
  
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 10 for calling the FISM obj's underlying member method code with example usage: obj.predict(interaction) and returns: output