class_docstr: EASE is a linear model for collaborative filtering, which combines the
  strengths of auto-encoders and neighborhood-based approaches.
code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID].cpu().numpy()
  
      r = self.interaction_matrix[user, :] @ self.item_similarity
      return torch.from_numpy(r.flatten())
  
file_path: RecBole/recbole/model/general_recommender/ease.py
imports: import torch
  import numpy
  import scipy.sparse
  from recbole.utils import InputType, ModelType
  from recbole.model.abstract_recommender import GeneralRecommender
label_desc: Use image node_id 5 for calling the EASE obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: torch