class_docstr: NPA model(Neural News Recommendation with Attentive Multi-View Learning)
  
  Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie:
  NPA: Neural News Recommendation with Personalized Attention, KDD 2019, ADS track.
  
  Attributes:
      word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
      hparam (object): Global hyper-parameters.
docstr: Initialization steps for MANL.
  Compared with the BaseModel, NPA need word embedding.
  After creating word embedding matrix, BaseModel's __init__ method will be called.
  
  Args:
      hparams (object): Global hyper-parameters. Some key setttings such as filter_num are there.
      iterator_creator_train (object): NPA data loader class for train data.
      iterator_creator_test (object): NPA data loader class for test and validation data
code: def __init__(self, hparams, iterator_creator, seed=None):
      """Initialization steps for MANL.
      Compared with the BaseModel, NPA need word embedding.
      After creating word embedding matrix, BaseModel's __init__ method will be called.
  
      Args:
          hparams (object): Global hyper-parameters. Some key setttings such as filter_num are there.
          iterator_creator_train (object): NPA data loader class for train data.
          iterator_creator_test (object): NPA data loader class for test and validation data
      """
  
      self.word2vec_embedding = self._init_embedding(
          hparams.wordEmb_file
      )
      self.hparam = hparams
  
      super().__init__(hparams, iterator_creator, seed=seed)
  
file_path: recommenders/recommenders/models/newsrec/models/npa.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 PersonalizedAttentivePooling
label_desc: Use image node_id 1 to create a new NPAModel object from inherited base classes: BaseModel with example: obj = NPAModel(hparams, iterator_creator, seed)