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: The main function to create news encoder of LSTUR.
  
  Args:
      embedding_layer (object): a word embedding layer.
  
  Return:
      object: the news encoder of LSTUR.
docstr_tok: ["The","main","function","to","create","news","encoder","of","LSTUR",".","Args",":","embedding_layer","(","object",")",":","a","word","embedding","layer",".","Return",":","object",":","the","news","encoder","of","LSTUR","."]
code: def _build_newsencoder(self, embedding_layer):
      """The main function to create news encoder of LSTUR.
  
      Args:
          embedding_layer (object): a word embedding layer.
  
      Return:
          object: the news encoder of LSTUR.
      """
      hparams = self.hparams
      sequences_input_title = keras.Input(
          shape=(hparams.title_size,), dtype="int32"
      )
      embedded_sequences_title = embedding_layer(sequences_input_title)
  
      y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
      y = layers.Conv1D(
          hparams.filter_num,
          hparams.window_size,
          activation=hparams.cnn_activation,
          padding="same",
          bias_initializer=keras.initializers.Zeros(),
          kernel_initializer=keras.initializers.glorot_uniform(
              seed=self.seed
          ),
      )(y)
      print(y)
      y = layers.Dropout(hparams.dropout)(y)
      y = layers.Masking()(
          OverwriteMasking()(
              [y, ComputeMasking()(sequences_input_title)]
          )
      )
      pred_title = AttLayer2(
          hparams.attention_hidden_dim, seed=self.seed
      )(y)
      print(pred_title)
      model = keras.Model(
          sequences_input_title, pred_title, name="news_encoder"
      )
      return model
  
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 7 for calling the LSTURModel obj's underlying member method code with example usage: obj._build_newsencoder(embedding_layer) and returns: model