code: def optimizer(self):
      assert (
          self.mode == "train"
      ), "optimizer only can be get in train mode"
      values = [
          self.learning_rate * (self.decay_gamma**i)
          for i in range(len(self.decay_epochs) + 1)
      ]
      iter_per_epoch = self.num_samples / self.batch_size
      boundaries = [e * iter_per_epoch for e in self.decay_epochs]
      return fluid.optimizer.RMSProp(
          learning_rate=fluid.layers.piecewise_decay(
              values=values, boundaries=boundaries
          ),
          centered=True,
          regularization=fluid.regularizer.L2Decay(self.weight_decay),
      )
  
file_path: PaddleHub/modules/video/classification/videotag_tsn_lstm/resource/models/attention_lstm/attention_lstm.py
imports: import logging
  import paddle.fluid
  from paddle.fluid import ParamAttr
  from ..model import ModelBase
  from .lstm_attention import LSTMAttentionModel
label_desc: Use image node_id 5 for calling the AttentionLSTM obj's underlying member method code with example usage: obj.optimizer() and returns: fluid