class_docstr: FOSSIL uses similarity of the items as main purpose and uses high MC as a way of sequential preference improve of
  ability of sequential recommendation
class_docstr_tok: ["FOSSIL","uses","similarity","of","the","items","as","main","purpose","and","uses","high","MC","as","a","way","of","sequential","preference","improve","of","ability","of","sequential","recommendation"]
code: def predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      test_item = interaction[self.ITEM_ID]
      user = interaction[self.USER_ID]
      seq_output = self.forward(item_seq, item_seq_len, user)
      test_item_emb = self.item_embedding(test_item)
      scores = torch.mul(seq_output, test_item_emb).sum(dim=1)
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/fossil.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 9 for calling the FOSSIL obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores