class_docstr: ENMF is an efficient non-sampling model for general recommendation.
  In order to run non-sampling model, please set the neg_sampling parameter as None .
class_docstr_tok: ["ENMF","is","an","efficient","non-sampling","model","for","general","recommendation",".","In","order","to","run","non-sampling","model",",","please","set","the","neg_sampling","parameter","as","None","."]
code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
  
      u_e = self.user_embedding(user)  # shape: [B,embedding_dim]
  
      all_i_e = (
          self.item_embedding.weight
      )  # shape: [n_item,embedding_dim]
  
      score = torch.mul(
          u_e.unsqueeze(1), all_i_e.unsqueeze(0)
      )  # shape: [B, n_item, embedding_dim]
  
      score = self.H_i(score).squeeze(2)  # shape: [B, n_item]
  
      return score.view(-1)
  
file_path: RecBole/recbole/model/general_recommender/enmf.py
imports: import torch
  import torch.nn
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
  from recbole.model.abstract_recommender import GeneralRecommender
label_desc: Use image node_id 6 for calling the ENMF obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: score