code: def get_deeper_side_obj(self):
      deeper_side_reward = self.reward[self.wider_seg_deeper :]
  
      # obj from deeper side
      deeper_side_obj = []
      for _i in range(self.deeper_actor.decision_num):
          decision_trajectory = self.deeper_decision_trajectory[:, _i]
          deeper_decision_mask = self.deeper_decision_mask[:, _i]
          decision_trajectory = tf.one_hot(
              decision_trajectory, depth=self.deeper_actor.out_dims[_i]
          )
          deeper_probs = tf.reduce_max(
              tf.multiply(
                  decision_trajectory, self.deeper_actor.probs[_i]
              ),
              axis=1,
          )
          deeper_probs = tf.log(deeper_probs)  # [deeper_batch_size]
          deeper_probs = tf.multiply(deeper_probs, deeper_decision_mask)
          deeper_probs = tf.multiply(deeper_probs, deeper_side_reward)
  
          deeper_side_obj.append(tf.reduce_sum(deeper_probs))
      deeper_side_obj = tf.reduce_sum(deeper_side_obj)
      return deeper_side_obj, self.get_deeper_entropy()
  
imports: from ..meta_controller.base_controller import WiderActorNet, DeeperActorNet, EncoderNet, BaseController
  import tensorflow
  import os
  from tensorflow.python.ops import array_ops
  from ..models.basic_model import BasicModel
  import shutil
  import numpy
label_desc: Use image node_id 3 for calling the ReinforceNet2NetController obj's underlying member method code with example usage: obj.get_deeper_side_obj() and returns: deeper_side_obj,  self