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 reg_loss(self, user_embedding, item_embedding, seq_output):
      reg_1 = self.reg_weight
      loss_1 = (
          reg_1 * torch.norm(user_embedding, p=2)
          + reg_1 * torch.norm(item_embedding, p=2)
          + reg_1 * torch.norm(seq_output, p=2)
      )
  
      return loss_1
  
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 3 for calling the FOSSIL obj's underlying member method code with example usage: obj.reg_loss(user_embedding, item_embedding, seq_output) and returns: loss_1