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 ave_net(item_seq, item_emb):
      mask = item_seq.gt(0)
      alpha = mask.to(torch.float) / mask.sum(dim=-1, keepdim=True)
      return alpha.unsqueeze(-1)
  
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 3 for calling the CORE obj's underlying member method code with example usage: obj.ave_net(item_seq, item_emb) and returns: alpha