code: def predict_unitary_actions_rho(self, observation):
      processed_obs = observation.reshape(1, -1)
      predicted_rho = self.fluid_executor.run(
          self.predict_program,
          feed={"obs": processed_obs},
          fetch_list=[self.predicted_unitary_actions_rho],
      )[0].reshape(-1)
      sorted_idx = np.argsort(predicted_rho)
      return sorted_idx.tolist(), predicted_rho
  
file_path: PARL/examples/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/es_agent.py
imports: import parl
  import paddle.fluid
  from parl import layers
  import numpy
label_desc: Use image node_id 3 for calling the ESAgent obj's underlying member method code with example usage: obj.predict_unitary_actions_rho(observation) and returns: sorted_idx,  predicted_rho