code: def predict(self, obs):
      dense_input = obs[:, :-2]
      month = obs[:, -2].astype("long")
      hour = obs[:, -1].astype("long")
      fluid.layers.reshape(month, (-1, 1), inplace=True)
      fluid.layers.reshape(hour, (-1, 1), inplace=True)
  
      month_emb = self.month_embedding(month)
      hour_emb = self.hour_embedding(hour)
      output = fluid.layers.concat(
          input=[dense_input, month_emb, hour_emb], axis=1
      )
  
      output1 = self.fc1(output)
      output2 = self.fc2(output1)
      output3 = self.fc3(output2)
      output4 = self.fc4(output3)
      output5 = self.fc5(output4)
      output6 = self.fc6(output5)
      return output6
  
file_path: PARL/examples/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track1/powernet_model.py
imports: import parl
  import paddle.fluid
  from parl import layers
label_desc: Use image node_id 2 for calling the UnitaryActionModel obj's underlying member method code with example usage: obj.predict(obs) and returns: output6