code: def predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
      result = (
          self.user_embeddings[user, :]
          * self.item_embeddings[:, item].T
      ).sum(axis=1)
      return result.float()
  
file_path: RecBole/recbole/model/general_recommender/nceplrec.py
imports: import torch
  import numpy
  import scipy.sparse
  from sklearn.utils.extmath import randomized_svd
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.utils import InputType
label_desc: Use image node_id 4 for calling the NCEPLRec obj's underlying member method code with example usage: obj.predict(interaction) and returns: result