code: def update_controller(
      self,
      learning_rate,
      wider_seg_deeper,
      wider_decision_trajectory,
      wider_decision_mask,
      deeper_decision_trajectory,
      deeper_decison_mask,
      reward,
      block_layer_num,
      input_seq,
      seq_len,
  ):
      has_deeper = wider_seg_deeper < len(input_seq)
      feed_dict = {
          self.learning_rate: learning_rate,
          self.wider_seg_deeper: wider_seg_deeper,
          self.wider_decision_trajectory: wider_decision_trajectory,
          self.wider_decision_mask: wider_decision_mask,
          self.deeper_decision_trajectory: deeper_decision_trajectory,
          self.deeper_decision_mask: deeper_decison_mask,
          self.reward: reward,
          self.is_training: True and has_deeper,
          self.deeper_actor.block_layer_num: block_layer_num,
          self.encoder.input_seq: input_seq,
          self.encoder.seq_len: seq_len,
          self.has_deeper: has_deeper,
      }
      self.sess.run(self.train_step, feed_dict=feed_dict)
  
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 5 for calling the RLNet2NetController obj's underlying member method code with example usage: obj.update_controller(learning_rate, wider_seg_deeper, wider_decision_trajectory, wider_decision_mask, deeper_decision_trajectory, deeper_decison_mask, reward, block_layer_num, input_seq, seq_len) without return types