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 calculate_loss(self, interaction):
      seq_item = interaction[self.ITEM_SEQ]
      user = interaction[self.USER_ID]
      seq_item_len = interaction[self.ITEM_SEQ_LEN]
      seq_output = self.forward(seq_item, seq_item_len, user)
      pos_items = interaction[self.POS_ITEM_ID]
      pos_items_emb = self.item_embedding(pos_items)
  
      user_lambda = self.user_lambda(user)
      pos_items_embedding = self.item_embedding(pos_items)
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          neg_items_emb = self.item_embedding(neg_items)
          pos_score = torch.sum(seq_output * pos_items_emb, dim=-1)
          neg_score = torch.sum(seq_output * neg_items_emb, dim=-1)
          loss = self.loss_fct(pos_score, neg_score)
          return loss + self.reg_loss(
              user_lambda, pos_items_embedding, seq_output
          )
      else:  # self.loss_type = 'CE'
          test_item_emb = self.item_embedding.weight
          logits = torch.matmul(
              seq_output, test_item_emb.transpose(0, 1)
          )
          loss = self.loss_fct(logits, pos_items)
          return loss + self.reg_loss(
              user_lambda, pos_items_embedding, seq_output
          )
  
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 8 for calling the FOSSIL obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: unknown, unknown