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 vert encoder of NAML news encoder.
  
  Return:
      object: the vert encoder of NAML.
docstr_tok: ["build","vert","encoder","of","NAML","news","encoder",".","Return",":","object",":","the","vert","encoder","of","NAML","."]
code: def _build_vertencoder(self):
      """build vert encoder of NAML news encoder.
  
      Return:
          object: the vert encoder of NAML.
      """
      hparams = self.hparams
      input_vert = keras.Input(shape=(1,), dtype="int32")
  
      vert_embedding = layers.Embedding(
          hparams.vert_num, hparams.vert_emb_dim, trainable=True
      )
  
      vert_emb = vert_embedding(input_vert)
      pred_vert = layers.Dense(
          hparams.filter_num,
          activation=hparams.dense_activation,
          bias_initializer=keras.initializers.Zeros(),
          kernel_initializer=keras.initializers.glorot_uniform(
              seed=self.seed
          ),
      )(vert_emb)
      pred_vert = layers.Reshape((1, hparams.filter_num))(pred_vert)
  
      model = keras.Model(input_vert, pred_vert, name="vert_encoder")
      return model
  
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 10 for calling the NAMLModel obj's underlying member method code with example usage: obj._build_vertencoder() and returns: model