class_docstr: GRU Model
  
  :Citation:
  
      Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, 
      Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning Phrase 
      Representations using RNN Encoder-Decoder for Statistical Machine Translation. 
      arXiv preprint arXiv:1406.1078. 2014.
docstr: The main function to create GRU model.
  
  Returns:
      object:the output of GRU section.
docstr_tok: ["The","main","function","to","create","GRU","model",".","Returns",":","object",":","the","output","of","GRU","section","."]
code: def _build_seq_graph(self):
      """The main function to create GRU model.
  
      Returns:
          object:the output of GRU section.
      """
      with tf.compat.v1.variable_scope("gru"):
          # final_state = self._build_lstm()
          final_state = self._build_gru()
          model_output = tf.concat(
              [final_state, self.target_item_embedding], 1
          )
          tf.compat.v1.summary.histogram("model_output", model_output)
          return model_output
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/gru.py
imports: import tensorflow
  from keras.layers.legacy_rnn.rnn_cell_impl import GRUCell, LSTMCell
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
  from tensorflow.compat.v1.nn import dynamic_rnn
label_desc: Use image node_id 1 for calling the GRUModel obj's underlying member method code with example usage: obj._build_seq_graph() and returns: model_output