class_docstr: NARM explores a hybrid encoder with an attention mechanism to model the user’s sequential behavior,
  and capture the user’s main purpose in the current session.
class_docstr_tok: ["NARM","explores","a","hybrid","encoder","with","an","attention","mechanism","to","model","the","user","\u2019","s","sequential","behavior",",","and","capture","the","user","\u2019","s","main","purpose","in","the","current","session","."]
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/narm.py
imports: import torch
  from torch import nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 6 for calling the NARM obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores