class_docstr: LSTUR model(Neural News Recommendation with Multi-Head Self-Attention)
  
  Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu and Xing Xie:
  Neural News Recommendation with Long- and Short-term User Representations, ACL 2019
  
  Attributes:
      word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
      hparam (object): Global hyper-parameters.
docstr: Initialization steps for LSTUR.
  Compared with the BaseModel, LSTUR 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 type and gru_unit are there.
      iterator_creator_train (object): LSTUR data loader class for train data.
      iterator_creator_test (object): LSTUR data loader class for test and validation data
docstr_tok: ["Initialization","steps","for","LSTUR",".","Compared","with","the","BaseModel",",","LSTUR","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","type","and","gru_unit","are","there",".","iterator_creator_train","(","object",")",":","LSTUR","data","loader","class","for","train","data",".","iterator_creator_test","(","object",")",":","LSTUR","data","loader","class","for","test","and","validation","data"]
code: def __init__(self, hparams, iterator_creator, seed=None):
      """Initialization steps for LSTUR.
      Compared with the BaseModel, LSTUR 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 type and gru_unit are there.
          iterator_creator_train (object): LSTUR data loader class for train data.
          iterator_creator_test (object): LSTUR 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/lstur.py
imports: import tensorflow.compat.v1.keras
  from tensorflow.compat.v1.keras import layers
  from recommenders.models.newsrec.models.base_model import BaseModel
  from recommenders.models.newsrec.models.layers import AttLayer2, ComputeMasking, OverwriteMasking
label_desc: Use image node_id 1 to create a new LSTURModel object from inherited base classes: BaseModel with example: obj = LSTURModel(hparams, iterator_creator, seed)