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 forward(self, item_seq):
      x = self.item_embedding(item_seq)
      x = self.sess_dropout(x)
      # Representation-Consistent Encoder (RCE)
      alpha = self.net(item_seq, x)
      seq_output = torch.sum(alpha * x, dim=1)
      seq_output = F.normalize(seq_output, dim=-1)
      return seq_output
  
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 4 for calling the CORE obj's underlying member method code with example usage: obj.forward(item_seq) and returns: seq_output