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 to the current predict item
docstr_tok: ["in","order","to","get","the","inference","of","past","items","to","the","current","predict","item"]
code: def get_similarity(self, seq_item_embedding, seq_item_len):
      """
      in order to get the inference of past items to the current predict item
      """
      coeff = torch.pow(seq_item_len.unsqueeze(1), -self.alpha).float()
      # batch_size * 1
      similarity = torch.mul(coeff, seq_item_embedding.sum(dim=1))
      # batch_size * embedding_size
  
      return 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 7 for calling the FOSSIL obj's underlying member method code with example usage: obj.get_similarity(seq_item_embedding, seq_item_len) and returns: similarity