class_docstr: NAML model(Neural News Recommendation with Attentive Multi-View Learning)
  
  Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie,
  Neural News Recommendation with Attentive Multi-View Learning, IJCAI 2019
  
  Attributes:
      word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
      hparam (object): Global hyper-parameters.
class_docstr_tok: ["NAML","model","(","Neural","News","Recommendation","with","Attentive","Multi-View","Learning",")","Chuhan","Wu",",","Fangzhao","Wu",",","Mingxiao","An",",","Jianqiang","Huang",",","Yongfeng","Huang","and","Xing","Xie",",","Neural","News","Recommendation","with","Attentive","Multi-View","Learning",",","IJCAI","2019","Attributes",":","word2vec_embedding","(","numpy.ndarray",")",":","Pretrained","word","embedding","matrix",".","hparam","(","object",")",":","Global","hyper-parameters","."]
docstr: Build NAML model and scorer.
  
  Returns:
      object: a model used to train.
      object: a model used to evaluate and inference.
docstr_tok: ["Build","NAML","model","and","scorer",".","Returns",":","object",":","a","model","used","to","train",".","object",":","a","model","used","to","evaluate","and","inference","."]
code: def _build_graph(self):
      """Build NAML model and scorer.
  
      Returns:
          object: a model used to train.
          object: a model used to evaluate and inference.
      """
  
      model, scorer = self._build_naml()
      return model, scorer
  
file_path: recommenders/recommenders/models/newsrec/models/naml.py
imports: import numpy
  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
label_desc: Use image node_id 5 for calling the NAMLModel obj's underlying member method code with example usage: obj._build_graph() and returns: model,  scorer