class_docstr: STAMP is capable of capturing users’ general interests from the long-term memory of a session context,
  whilst taking into account users’ current interests from the short-term memory of the last-clicks.
  
  
  Note:
  
      According to the test results, we made a little modification to the score function mentioned in the paper,
      and did not use the final sigmoid activation function.
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code: def full_sort_predict(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)
      test_items_emb = self.item_embedding.weight
      scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1))
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/stamp.py
imports: import torch
  from torch import nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 7 for calling the STAMP obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores