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 and labels for trainning from iterator
  
  Args:
      batch data: input batch data from iterator
  
  Returns:
      list: input feature fed into model (clicked_title_batch & candidate_title_batch)
      numpy.ndarray: labels
docstr_tok: ["get","input","and","labels","for","trainning","from","iterator","Args",":","batch","data",":","input","batch","data","from","iterator","Returns",":","list",":","input","feature","fed","into","model","(","clicked_title_batch","&","candidate_title_batch",")","numpy.ndarray",":","labels"]
code: def _get_input_label_from_iter(self, batch_data):
      """get input and labels for trainning from iterator
  
      Args:
          batch data: input batch data from iterator
  
      Returns:
          list: input feature fed into model (clicked_title_batch & candidate_title_batch)
          numpy.ndarray: labels
      """
      input_feat = [
          batch_data["clicked_title_batch"],
          batch_data["candidate_title_batch"],
      ]
      input_label = batch_data["labels"]
      return input_feat, input_label
  
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 2 for calling the NRMSModel obj's underlying member method code with example usage: obj._get_input_label_from_iter(batch_data) and returns: input_feat,  input_label