class_docstr: models a user’s click to an item in two terms: the personal preference of the user for the item,
  and the relationships between this item and other items clicked by the user
class_docstr_tok: ["models","a","user","\u2019","s","click","to","an","item","in","two","terms",":","the","personal","preference","of","the","user","for","the","item",",","and","the","relationships","between","this","item","and","other","items","clicked","by","the","user"]
code: def calculate_loss(self, interaction):
      seq_item = interaction[self.ITEM_SEQ]
      user = interaction[self.USER_ID]
      seq_output = self.forward(seq_item, user)
      pos_items = interaction[self.POS_ITEM_ID]
      pos_items_embs = self.item_embedding(pos_items)
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          neg_items_emb = self.relu(self.item_embedding(neg_items))
          pos_items_emb = self.relu(pos_items_embs)
          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
      else:  # self.loss_type = 'CE'
          test_item_emb = self.relu(self.item_embedding.weight)
          logits = torch.matmul(
              seq_output, test_item_emb.transpose(0, 1)
          )
          loss = self.loss_fct(logits, pos_items)
          return loss
  
file_path: RecBole/recbole/model/sequential_recommender/npe.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 4 for calling the NPE obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss