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"]
docstr: in order to get the inference of past items and the user's taste to the current predict item
docstr_tok: ["in","order","to","get","the","inference","of","past","items","and","the","user","'s","taste","to","the","current","predict","item"]
code: def get_high_order_Markov(self, high_order_item_embedding, user):
      """
  
      in order to get the inference of past items and the user's taste to the current predict item
      """
  
      user_lambda = self.user_lambda(user).unsqueeze(dim=2)
      # batch_size * order_len * 1
      lambda_ = self.lambda_.unsqueeze(dim=0).unsqueeze(dim=2)
      # 1 * order_len * 1
      lambda_ = torch.add(user_lambda, lambda_)
      # batch_size * order_len * 1
      high_order_item_embedding = torch.mul(
          high_order_item_embedding, lambda_
      )
      # batch_size * order_len * embedding_size
      high_order_item_embedding = high_order_item_embedding.sum(dim=1)
      # batch_size * embedding_size
  
      return high_order_item_embedding
  
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 6 for calling the FOSSIL obj's underlying member method code with example usage: obj.get_high_order_Markov(high_order_item_embedding, user) and returns: high_order_item_embedding