code: def _define_input(self):
      self.learning_rate = tf.placeholder(
          tf.float32, shape=[], name="learning_rate"
      )
      self.is_training = tf.placeholder(
          tf.bool, shape=[], name="is_training"
      )
      self.wider_seg_deeper = tf.placeholder(
          tf.int32, shape=[], name="wider_seg_deeper"
      )
  
      self.wider_decision_trajectory = tf.placeholder(
          tf.int32,
          shape=[None, self.encoder.num_steps],
          name="wider_decision_trajectory",
      )  # [wider_batch_size, num_steps]
      self.wider_decision_mask = tf.placeholder(
          tf.float32,
          shape=[None, self.encoder.num_steps],
          name="wider_decision_mask",
      )  # [wider_batch_size, num_steps]
  
      self.deeper_decision_trajectory = tf.placeholder(
          tf.int32,
          shape=[None, self.deeper_actor.decision_num],
          name="deeper_decision_trajectory",
      )  # [deeper_batch_size, deeper_decision_num]
  
      self.deeper_decision_mask = tf.placeholder(
          tf.float32,
          shape=[None, self.deeper_actor.decision_num],
          name="deeper_decision_mask",
      )  # [deeper_batch_size, deeper_decision_num]
  
      self.reward = tf.placeholder(
          tf.float32,
          shape=[None],
          name="reward",
      )  # [batch_size]
      self.has_deeper = tf.placeholder(
          tf.bool,
          shape=[],
          name="has_deeper",
      )
  
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 4 for calling the RLNet2NetController obj's underlying member method code with example usage: obj._define_input() without return types