class_docstr: SRGNN regards the conversation history as a directed graph.
  In addition to considering the connection between the item and the adjacent item,
  it also considers the connection with other interactive items.
  
  Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connection matrix A
  
  Outgoing edges:
      === ===== ===== ===== =====
       \    1     2     3     4
      === ===== ===== ===== =====
       1    0     1     0     0
       2    0     0    1/2   1/2
       3    0     1     0     0
       4    0     0     0     0
      === ===== ===== ===== =====
  
  Incoming edges:
      === ===== ===== ===== =====
       \    1     2     3     4
      === ===== ===== ===== =====
       1    0     0     0     0
       2   1/2    0    1/2    0
       3    0     1     0     0
       4    0     1     0     0
      === ===== ===== ===== =====
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
      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
  
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.loss import BPRLoss
label_desc: Use image node_id 5 for calling the SRGNN obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss