code: def __init__(
      self,
      num_layers,
      input_size,
      hidden_size,
      dropout_prob=0.0,
      init_scale=0.1,
  ):
      super(AttentionDecoderCell, self).__init__()
      self.num_layers = num_layers
      self.hidden_size = hidden_size
      self.dropout_prob = dropout_prob
      self.lstm_cells = []
      self.init_scale = init_scale
      for i in range(num_layers):
          self.lstm_cells.append(
              LSTMCell(
                  input_size=input_size + hidden_size
                  if i == 0
                  else hidden_size,
                  hidden_size=hidden_size,
              )
          )
  
imports: import time
  from collections import OrderedDict
  import numpy
  import paddle
  import paddle.nn
  from paddle import ParamAttr
  from paddle.nn import BeamSearchDecoder
  from paddle.nn import dynamic_decode
  from paddle.nn import LSTMCell
  from paddle.nn import RNN
  from paddle.nn import RNNCellBase
  from paddlehub.compat.task.base_task import BaseTask
  from paddlehub.compat.task.metrics import compute_bleu
label_desc: Use image node_id 1 to create a new AttentionDecoderCell object from inherited base classes: RNNCellBase with example: obj = AttentionDecoderCell(num_layers, input_size, hidden_size, dropout_prob, init_scale)