code: def _ensemble_predict(self, obs):
      actor_outputs = []
      for i in range(self.ensemble_num):
          actor_outputs.append(self.actors[i].predict(obs))
      batch_actions = layers.concat(actor_outputs, axis=0)
      batch_obs = layers.expand(
          obs, expand_times=[self.ensemble_num, 1]
      )
  
      critic_outputs = []
      for i in range(self.ensemble_num):
          critic_output = self.critics[i].predict(
              batch_obs, batch_actions
          )
          critic_output = layers.unsqueeze(critic_output, axes=[1])
          critic_outputs.append(critic_output)
      score_matrix = layers.concat(critic_outputs, axis=1)
  
      # Normalize scores given by each critic
      sum_critic_score = layers.reduce_sum(
          score_matrix, dim=0, keep_dim=True
      )
      sum_critic_score = layers.expand(
          sum_critic_score, expand_times=[self.ensemble_num, 1]
      )
      norm_score_matrix = score_matrix / sum_critic_score
  
      actions_mean_score = layers.reduce_mean(
          norm_score_matrix, dim=1, keep_dim=True
      )
      best_score_id = layers.argmax(actions_mean_score, axis=0)
      best_score_id = layers.cast(best_score_id, dtype="int32")
      ensemble_predict_action = layers.gather(
          batch_actions, best_score_id
      )
      ensemble_predict_action = layers.squeeze(
          ensemble_predict_action, axes=[0]
      )
      return ensemble_predict_action
  
imports: import numpy
  import parl.layers
  from mlp_model import ActorModel, CriticModel
  from paddle import fluid
  from parl.utils import logger
label_desc: Use image node_id 4 for calling the EnsembleBaseModel obj's underlying member method code with example usage: obj._ensemble_predict(obs) and returns: ensemble_predict_action