code: def __init__(
      self,
      n_uid,
      n_mid,
      n_cat,
      EMBEDDING_DIM,
      HIDDEN_SIZE,
      ATTENTION_SIZE,
      lr,
      data_type="FP32",
      use_negsampling=False,
  ):
      super(Model_DIN_V2_Gru_QA_attGru, self).__init__(
          n_uid,
          n_mid,
          n_cat,
          EMBEDDING_DIM,
          HIDDEN_SIZE,
          ATTENTION_SIZE,
          lr,
          data_type,
          use_negsampling,
      )
  
      # RNN layer(-s)
      with tf.name_scope("rnn_1"):
          rnn_outputs, _ = dynamic_rnn(
              GRUCell(HIDDEN_SIZE),
              inputs=self.item_his_eb,
              sequence_length=self.seq_len_ph,
              dtype=tf.float32,
              scope="gru1",
          )
          tf.summary.histogram("GRU_outputs", rnn_outputs)
  
      # Attention layer
      with tf.name_scope("Attention_layer_1"):
          att_outputs, alphas = din_fcn_attention(
              self.item_eb,
              rnn_outputs,
              ATTENTION_SIZE,
              self.mask,
              softmax_stag=1,
              stag="1_1",
              mode="LIST",
              return_alphas=True,
          )
          tf.summary.histogram("alpha_outputs", alphas)
  
      with tf.name_scope("rnn_2"):
          rnn_outputs2, final_state2 = dynamic_rnn(
              QAAttGRUCell(HIDDEN_SIZE),
              inputs=rnn_outputs,
              att_scores=tf.expand_dims(alphas, -1),
              sequence_length=self.seq_len_ph,
              dtype=self.model_dtype,
              scope="gru2",
          )
          tf.summary.histogram("GRU2_Final_State", final_state2)
  
      inp = tf.concat(
          [
              self.uid_batch_embedded,
              self.item_eb,
              self.item_his_eb_sum,
              self.item_eb * self.item_his_eb_sum,
              final_state2,
          ],
          1,
      )
      self.build_fcn_net(inp, use_dice=True)
  
imports: import tensorflow
  from rnn import dynamic_rnn
  from utils import *
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new Model_DIN_V2_Gru_QA_attGru object from inherited base classes: Model with example: obj = Model_DIN_V2_Gru_QA_attGru(n_uid, n_mid, n_cat, EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE, lr, data_type, use_negsampling)