class_docstr: NRMS model(Neural News Recommendation with Multi-Head Self-Attention)
  
  Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang,and Xing Xie, "Neural News
  Recommendation with Multi-Head Self-Attention" in Proceedings of the 2019 Conference
  on Empirical Methods in Natural Language Processing and the 9th International Joint Conference
  on Natural Language Processing (EMNLP-IJCNLP)
  
  Attributes:
      word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
      hparam (object): Global hyper-parameters.
docstr: get input of user encoder
  Args:
      batch_data: input batch data from user iterator
  
  Returns:
      numpy.ndarray: input user feature (clicked title batch)
docstr_tok: ["get","input","of","user","encoder","Args",":","batch_data",":","input","batch","data","from","user","iterator","Returns",":","numpy.ndarray",":","input","user","feature","(","clicked","title","batch",")"]
code: def _get_user_feature_from_iter(self, batch_data):
      """get input of user encoder
      Args:
          batch_data: input batch data from user iterator
  
      Returns:
          numpy.ndarray: input user feature (clicked title batch)
      """
      return batch_data["clicked_title_batch"]
  
file_path: recommenders/recommenders/models/newsrec/models/nrms.py
imports: import tensorflow.keras
  from tensorflow.keras import layers
  from recommenders.models.newsrec.models.base_model import BaseModel
  from recommenders.models.newsrec.models.layers import AttLayer2, SelfAttention
label_desc: Use image node_id 3 for calling the NRMSModel obj's underlying member method code with example usage: obj._get_user_feature_from_iter(batch_data) and returns: batch_data