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 predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      seq_item_len = interaction[self.ITEM_SEQ_LEN]
      test_item = interaction[self.ITEM_ID]
      user = interaction[self.USER_ID]
      seq_output = self.forward(item_seq, user, seq_item_len)
      test_item_emb = self.item_embedding(test_item)
      scores = torch.mul(seq_output, test_item_emb).sum(dim=1)
  
      return scores
  
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 6 for calling the HRM obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores