code: def __init__(
      self,
      n_uid,
      n_mid,
      n_cat,
      EMBEDDING_DIM,
      HIDDEN_SIZE,
      ATTENTION_SIZE,
      lr,
      data_type="FP32",
      use_negsampling=True,
  ):
      super(Model_DIN_V2_Gru_Vec_attGru_Neg, self).__init__(
          n_uid,
          n_mid,
          n_cat,
          EMBEDDING_DIM,
          HIDDEN_SIZE,
          ATTENTION_SIZE,
          lr,
          data_type,
          use_negsampling,
      )
  
      def dtype_getter(getter, name, dtype=None, *args, **kwargs):
          var = getter(name, dtype=self.model_dtype, *args, **kwargs)
          return var
  
      with tf.variable_scope(
          "dien", custom_getter=dtype_getter, dtype=self.model_dtype
      ):
          # 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=self.model_dtype,
                  scope="gru1",
              )
              tf.summary.histogram("GRU_outputs", rnn_outputs)
  
          aux_loss_1 = self.auxiliary_loss(
              rnn_outputs[:, :-1, :],
              self.item_his_eb[:, 1:, :],
              self.noclk_item_his_eb[:, 1:, :],
              self.mask[:, 1:],
              stag="gru",
          )
          self.aux_loss = aux_loss_1
  
          # 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(
                  VecAttGRUCell(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_Vec_attGru_Neg object from inherited base classes: Model with example: obj = Model_DIN_V2_Gru_Vec_attGru_Neg(n_uid, n_mid, n_cat, EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE, lr, data_type, use_negsampling)