class_docstr: It is an extension of GRU4Rec, which concatenates item and its corresponding
  pre-trained knowledge graph embedding feature as the input.
class_docstr_tok: ["It","is","an","extension","of","GRU4Rec",",","which","concatenates","item","and","its","corresponding","pre-trained","knowledge","graph","embedding","feature","as","the","input","."]
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)  # [B H]
          neg_items_emb = self.item_embedding(neg_items)  # [B H]
          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
      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
  
file_path: RecBole/recbole/model/sequential_recommender/gru4reckg.py
imports: import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 3 for calling the GRU4RecKG obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss