code: def calculate_loss(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      seq_output = self.forward(item_seq, item_seq_len)
      pos_items = interaction[self.POS_ITEM_ID]
  
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          pos_items_emb = self.item_embedding(pos_items)
          neg_items_emb = self.item_embedding(neg_items)
          pos_score = torch.sum(
              seq_output * pos_items_emb, dim=-1
          )  # [B]
          neg_score = torch.sum(
              seq_output * neg_items_emb, dim=-1
          )  # [B]
          loss = self.loss_fct(pos_score, neg_score)
          return loss
      elif 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
      else:
          test_item_emb = self.item_embedding.weight
          logits = torch.matmul(
              seq_output, test_item_emb.transpose(0, 1)
          )
          logits = F.log_softmax(logits, dim=1)
          loss = self.loss_fct(logits, pos_items)
          return loss + self.calculate_reg_loss() * self.reg_loss_ratio
  
file_path: RecBole/recbole/model/sequential_recommender/sine.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.utils import InputType
label_desc: Use image node_id 4 for calling the SINE obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss, unknown