code: def forward(
      self, step_input, states, enc_output, enc_padding_mask=None
  ):
      lstm_states, input_feed = states
      new_lstm_states = []
      step_input = paddle.concat([step_input, input_feed], 1)
      for i in range(self.num_layers):
          out, new_lstm_state = self.lstm_cells[i](
              step_input, lstm_states[i]
          )
          step_input = (
              nn.functional.dropout(
                  out, self.dropout_prob, mode="upscale_in_train"
              )
              if self.dropout_prob > 0
              else out
          )
          new_lstm_states.append(new_lstm_state)
      dec_att = self.attention(step_input, enc_output, enc_padding_mask)
      dec_att = paddle.squeeze(dec_att, [1])
      concat_att_out = paddle.concat([dec_att, step_input], 1)
      out = paddle.static.nn.fc(
          concat_att_out,
          self.hidden_size,
          weight_attr=ParamAttr(
              name="dec_out_w",
              initializer=nn.initializer.Uniform(
                  low=-self.init_scale, high=self.init_scale
              ),
          ),
      )
      return out, [new_lstm_states, out]
  
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 3 for calling the AttentionDecoderCell obj's underlying member method code with example usage: obj.forward(step_input, states, enc_output, enc_padding_mask) and returns: out,  list