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 forward(self, seq_item, seq_item_len, user):
      seq_item_embedding = self.item_embedding(seq_item)
  
      high_order_seq_item_embedding = self.inverse_seq_item_embedding(
          seq_item_embedding, seq_item_len
      )
      # batch_size * order_len * embedding
  
      high_order = self.get_high_order_Markov(
          high_order_seq_item_embedding, user
      )
      similarity = self.get_similarity(seq_item_embedding, seq_item_len)
  
      return high_order + similarity
  
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 5 for calling the FOSSIL obj's underlying member method code with example usage: obj.forward(seq_item, seq_item_len, user) and returns: unknown