class_docstr: GCSAN captures rich local dependencies via graph neural network,
   and learns long-range dependencies by applying the self-attention mechanism.
  
  Note:
  
      In the original paper, the attention mechanism in the self-attention layer is a single head,
      for the reusability of the project code, we use a unified transformer component.
      According to the experimental results, we only applied regularization to embedding.
class_docstr_tok: ["GCSAN","captures","rich","local","dependencies","via","graph","neural","network",",","and","learns","long-range","dependencies","by","applying","the","self-attention","mechanism",".","Note",":","In","the","original","paper",",","the","attention","mechanism","in","the","self-attention","layer","is","a","single","head",",","for","the","reusability","of","the","project","code",",","we","use","a","unified","transformer","component",".","According","to","the","experimental","results",",","we","only","applied","regularization","to","embedding","."]
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)
      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)
  
      reg_loss = self.reg_loss(self.item_embedding.weight)
      total_loss = loss + self.reg_weight * reg_loss
      return total_loss
  
imports: import math
  import numpy
  import torch
  from torch import nn
  from torch.nn import Parameter
  from torch.nn import functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
  from recbole.model.loss import EmbLoss, BPRLoss
label_desc: Use image node_id 5 for calling the GCSAN obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: total_loss