code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID].cpu().numpy()
  
      user_interactions = self.interaction_matrix[user, :].toarray()
  
      if self.center_columns:
          r = (
              (user_interactions - self.item_means)
              @ self.item_similarity
              + self.item_means
          ).flatten()
      else:
          r = (user_interactions @ self.item_similarity).flatten()
  
      return add_noise(torch.from_numpy(r))
  
file_path: RecBole/recbole/model/general_recommender/admmslim.py
imports: from recbole.utils.enum_type import ModelType
  import numpy
  import scipy.sparse
  import torch
  from recbole.utils import InputType
  from recbole.model.abstract_recommender import GeneralRecommender
label_desc: Use image node_id 5 for calling the ADMMSLIM obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: add_noise