class_docstr: NeuMF is an neural network enhanced matrix factorization model.
  It replace the dot product to mlp for a more precise user-item interaction.
  
  Note:
  
      Our implementation only contains a rough pretraining function.
class_docstr_tok: ["NeuMF","is","an","neural","network","enhanced","matrix","factorization","model",".","It","replace","the","dot","product","to","mlp","for","a","more","precise","user-item","interaction",".","Note",":","Our","implementation","only","contains","a","rough","pretraining","function","."]
code: def calculate_loss(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
      label = interaction[self.LABEL]
  
      output = self.forward(user, item)
      return self.loss(output, label)
  
file_path: RecBole/recbole/model/general_recommender/neumf.py
imports: import torch
  import torch.nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.layers import MLPLayers
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the NeuMF obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: self