class_docstr: KSR integrates the RNN-based networks with Key-Value Memory Network (KV-MN).
  And it further incorporates knowledge base (KB) information to enhance the semantic representation of KV-MN.
class_docstr_tok: ["KSR","integrates","the","RNN-based","networks","with","Key-Value","Memory","Network","(","KV-MN",")",".","And","it","further","incorporates","knowledge","base","(","KB",")","information","to","enhance","the","semantic","representation","of","KV-MN","."]
code: def _get_item_comb_embedding(self, item):
      h_e, _ = self._get_kg_embedding(item)  # [B K]
      i_e = self.item_embedding(item)  # [B E]
      q_i = self.dense_layer_i(torch.cat((i_e, h_e), -1))  # [B E]
      return q_i
  
file_path: RecBole/recbole/model/sequential_recommender/ksr.py
imports: import torch
  from torch import nn
  from torch.nn.init import xavier_uniform_, xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 8 for calling the KSR obj's underlying member method code with example usage: obj._get_item_comb_embedding(item) and returns: q_i