class_docstr: CORE is a simple and effective framewor, which unifies the representation spac
  for both the encoding and decoding processes in session-based recommendation.
class_docstr_tok: ["CORE","is","a","simple","and","effective","framewor",",","which","unifies","the","representation","spac","for","both","the","encoding","and","decoding","processes","in","session-based","recommendation","."]
code: def predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      test_item = interaction[self.ITEM_ID]
      seq_output = self.forward(item_seq)
      test_item_emb = self.item_embedding(test_item)
      scores = (
          torch.mul(seq_output, test_item_emb).sum(dim=1)
          / self.temperature
      )
      return scores
  
imports: import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
label_desc: Use image node_id 6 for calling the CORE obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores