class_docstr: NGCF is a model that incorporate GNN for recommendation.
  We implement the model following the original author with a pairwise training mode.
class_docstr_tok: ["NGCF","is","a","model","that","incorporate","GNN","for","recommendation",".","We","implement","the","model","following","the","original","author","with","a","pairwise","training","mode","."]
code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
      if self.restore_user_e is None or self.restore_item_e is None:
          self.restore_user_e, self.restore_item_e = self.forward()
      # get user embedding from storage variable
      u_embeddings = self.restore_user_e[user]
  
      # dot with all item embedding to accelerate
      scores = torch.matmul(
          u_embeddings, self.restore_item_e.transpose(0, 1)
      )
  
      return scores.view(-1)
  
file_path: RecBole/recbole/model/general_recommender/ngcf.py
imports: import numpy
  import scipy.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.layers import BiGNNLayer, SparseDropout
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 8 for calling the NGCF obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores