code: def apply_deeper_decision(deeper_decision, net_configs, noise):
      if len(net_configs) == 1:
          decision = deeper_decision[0]
          net_config = net_configs[0]
  
          block_decision, layer_idx_decision = decision
          decision_mask = [1.0, 1.0]
          block_idx, _pt = 0, 0
          for _i, block in enumerate(net_config.blocks):
              if isinstance(block, DenseBlock):
                  if _pt == block_decision:
                      block_idx = _i
                      break
                  _pt += 1
          net_config.insert_miniblock(
              loc={"block": block_idx, "miniblock": layer_idx_decision},
              miniblock_config={"bc_mode": True},
              noise=noise,
          )
          return np.array([decision_mask])
      else:
          decision_mask = []
          for _i, net_config in enumerate(net_configs):
              decision = deeper_decision[_i]
              mask = apply_deeper_decision(
                  [decision], [net_config], noise
              )
              decision_mask.append(mask)
          return np.concatenate(decision_mask, axis=0)
  
file_path: auptimizer/src/aup/Proposer/eas/arch_search/arch_search_densenet_net2net.py
imports: from ..expdir_monitor.arch_manager import ArchManager
  from ..meta_controller.base_controller import Vocabulary, EncoderNet, WiderActorNet, DeeperActorNet
  from ..meta_controller.rl_controller import ReinforceNet2NetController
  from time import gmtime, strftime, time
  from datetime import timedelta
  from ..models.dense_net import DenseBlock, TransitionBlock
  import re
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: apply_deeper_decision(deeper_decision, net_configs, noise) and returns: np, np