code: def _predict_unitary_action_rho(self, observation):
      processed_obs = self.unitary_feature_processor.process(
          self.observation
      )
      processed_obs = torch.as_tensor(processed_obs).view(1, -1)
      predicted_rho = (
          self.unitary_model(processed_obs).detach().cpu().numpy()[0]
      )
      return predicted_rho
  
imports: import torch
  import numpy
  import pickle
  import os
  from grid2op.Agent import BaseAgent
  from copy import deepcopy
  from powernet_model import CombinedActionModel, UnitaryActionModel
  from utils import FeatureProcessor, UnitaryFeatureProcessor
label_desc: Use image node_id 8 for calling the RLAgent obj's underlying member method code with example usage: obj._predict_unitary_action_rho(observation) and returns: predicted_rho