class_docstr: DKN model (Deep Knowledge-Aware Network)
  
  :Citation:
  
      H. Wang, F. Zhang, X. Xie and M. Guo, "DKN: Deep Knowledge-Aware Network for News
      Recommendation", in Proceedings of the 2018 World Wide Web Conference on World
      Wide Web, 2018.
class_docstr_tok: ["DKN","model","(","Deep","Knowledge-Aware","Network",")",":","Citation",":","H.","Wang",",","F.","Zhang",",","X.","Xie","and","M.","Guo",",","``","DKN",":","Deep","Knowledge-Aware","Network","for","News","Recommendation","''",",","in","Proceedings","of","the","2018","World","Wide","Web","Conference","on","World","Wide","Web",",","2018","."]
docstr: Infer document embedding in feed_dict with current model.
  
  Args:
      sess (object): The model session object.
      feed_dict (dict): Feed values for evaluation. This is a dictionary that maps graph elements to values.
  
  Returns:
      list: News embedding in a batch.
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code: def infer_embedding(self, sess, feed_dict):
      """Infer document embedding in feed_dict with current model.
  
      Args:
          sess (object): The model session object.
          feed_dict (dict): Feed values for evaluation. This is a dictionary that maps graph elements to values.
  
      Returns:
          list: News embedding in a batch.
      """
      feed_dict[self.layer_keeps] = self.keep_prob_test
      feed_dict[self.is_train_stage] = False
      return sess.run(
          [self.news_field_embed_final_batch], feed_dict=feed_dict
      )
  
file_path: recommenders/recommenders/models/deeprec/models/dkn.py
imports: import numpy
  import tensorflow
  from recommenders.models.deeprec.models.base_model import BaseModel
label_desc: Use image node_id 9 for calling the DKN obj's underlying member method code with example usage: obj.infer_embedding(sess, feed_dict) and returns: sess