class_docstr: EASE is a linear model for collaborative filtering, which combines the
  strengths of auto-encoders and neighborhood-based approaches.
code: def predict(self, interaction):
      user = interaction[self.USER_ID].cpu().numpy()
      item = interaction[self.ITEM_ID].cpu().numpy()
  
      return torch.from_numpy(
          (
              self.interaction_matrix[user, :].multiply(
                  self.item_similarity[:, item].T
              )
          )
          .sum(axis=1)
          .getA1()
      ).to(self.device)
  
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 4 for calling the EASE obj's underlying member method code with example usage: obj.predict(interaction) and returns: torch