code: def predict(self, obs):
      continuous_obs = fluid.layers.slice(
          obs, axes=[1], starts=[0], ends=[-2 - line_num]
      )
      line_status = fluid.layers.slice(
          obs, axes=[1], starts=[-2 - line_num], ends=[-2]
      ).astype("long")
      line_status = fluid.layers.reshape(line_status, shape=(-1, 1))
      month = obs[:, -1].astype("long")
      hour = obs[:, -2].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)
      line_status_emb = self.line_status_embedding(line_status)
      line_status_emb = fluid.layers.reshape(
          line_status_emb, shape=(-1, line_num, 256)
      )
      line_status_emb_sum = fluid.layers.reduce_sum(
          line_status_emb, dim=1
      )
      output = fluid.layers.concat(
          input=[
              continuous_obs,
              hour_emb,
              month_emb,
              line_status_emb_sum,
          ],
          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/track2/powernet_model.py
imports: import parl
  import numpy
  import paddle.fluid
  from parl import layers
  import os
label_desc: Use image node_id 2 for calling the PowerNetModel obj's underlying member method code with example usage: obj.predict(obs) and returns: output6