code: def build_model(self):
      att_outs = []
      for i, (input_dim, feature) in enumerate(
          zip(self.feature_dims, self.feature_input)
      ):
          att = LSTMAttentionModel(
              input_dim,
              self.embedding_size,
              self.lstm_size,
              self.drop_rate,
          )
          att_out = att.forward(
              feature, is_training=(self.mode == "train")
          )
          att_outs.append(att_out)
      if len(att_outs) > 1:
          out = fluid.layers.concat(att_outs, axis=1)
      else:
          out = att_outs[0]
  
      fc1 = fluid.layers.fc(
          input=out,
          size=8192,
          act="relu",
          bias_attr=ParamAttr(
              regularizer=fluid.regularizer.L2Decay(0.0),
              initializer=fluid.initializer.NormalInitializer(
                  scale=0.0
              ),
          ),
          name="fc1",
      )
      fc2 = fluid.layers.fc(
          input=fc1,
          size=4096,
          act="tanh",
          bias_attr=ParamAttr(
              regularizer=fluid.regularizer.L2Decay(0.0),
              initializer=fluid.initializer.NormalInitializer(
                  scale=0.0
              ),
          ),
          name="fc2",
      )
  
      self.logit = fluid.layers.fc(
          input=fc2,
          size=self.num_classes,
          act=None,
          bias_attr=ParamAttr(
              regularizer=fluid.regularizer.L2Decay(0.0),
              initializer=fluid.initializer.NormalInitializer(
                  scale=0.0
              ),
          ),
          name="output",
      )
  
      self.output = fluid.layers.sigmoid(self.logit)
  
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 4 for calling the AttentionLSTM obj's underlying member method code with example usage: obj.build_model() without return types