code: def sample_deeper_decision(self, input_seq, seq_len, block_layer_num):
      deeper_decision, deeper_probs = self.sess.run(
          fetches=[self.deeper_actor.decision, self.deeper_actor.probs],
          feed_dict={
              self.encoder.input_seq: input_seq,
              self.encoder.seq_len: seq_len,
              self.wider_seg_deeper: 0,
              self.is_training: False,
              self.deeper_actor.block_layer_num: block_layer_num,
              self.deeper_decision_trajectory: -np.ones(
                  [len(seq_len), self.deeper_actor.decision_num]
              ),
          },
      )  # [batch_size, decision_num]
      return deeper_decision, deeper_probs
  
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 9 for calling the RLNet2NetController obj's underlying member method code with example usage: obj.sample_deeper_decision(input_seq, seq_len, block_layer_num) and returns: deeper_decision,  deeper_probs