class_docstr: HRM can well capture both sequential behavior and users’ general taste by involving transaction and
  user representations in prediction.
  
  HRM user max- & average- pooling as a good helper.
code: def calculate_loss(self, interaction):
      seq_item = interaction[self.ITEM_SEQ]
      seq_item_len = interaction[self.ITEM_SEQ_LEN]
      user = interaction[self.USER_ID]
      seq_output = self.forward(seq_item, user, seq_item_len)
      pos_items = interaction[self.POS_ITEM_ID]
      pos_items_emb = self.item_embedding(pos_items)
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          neg_items_emb = self.item_embedding(neg_items)
          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.item_embedding.weight.t()
          logits = torch.matmul(seq_output, test_item_emb)
          loss = self.loss_fct(logits, pos_items)
  
          return loss
  
file_path: RecBole/recbole/model/sequential_recommender/hrm.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 5 for calling the HRM obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss