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","."]
docstr: A simple implementation of dumping model parameters for pretrain.
docstr_tok: ["A","simple","implementation","of","dumping","model","parameters","for","pretrain","."]
code: def dump_parameters(self):
      r"""A simple implementation of dumping model parameters for pretrain."""
      if self.mf_train and not self.mlp_train:
          save_path = self.mf_pretrain_path
          torch.save(self, save_path)
      elif self.mlp_train and not self.mf_train:
          save_path = self.mlp_pretrain_path
          torch.save(self, save_path)
  
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 7 for calling the NeuMF obj's underlying member method code with example usage: obj.dump_parameters() without return types