code: def mutate_architecture(
      self, parent_arch: ModelMicroCfg
  ) -> (str, ModelMicroCfg):
      assert isinstance(parent_arch, MlpMicroCfg)
      assert isinstance(self.model_cfg, MlpMacroCfg)
      child_layer_list = deepcopy(parent_arch.hidden_layer_list)
  
      # 1. choose layer index
      chosen_hidden_layer_index = random.choice(
          list(range(len(child_layer_list)))
      )
  
      # 2. choose size of the layer index, increase the randomness
      while True:
          cur_layer_size = child_layer_list[chosen_hidden_layer_index]
          mutated_layer_size = random.choice(
              self.model_cfg.layer_choices
          )
          if mutated_layer_size != cur_layer_size:
              child_layer_list[
                  chosen_hidden_layer_index
              ] = mutated_layer_size
              new_model = MlpMicroCfg(child_layer_list)
              return str(new_model), new_model
  
imports: import copy
  import itertools
  import random
  import time
  from copy import deepcopy
  from typing import Generator
  from src.common.constant import Config, CommonVars
  from src.eva_engine import evaluator_register
  from src.eva_engine.phase2.algo.trainer import ModelTrainer
  from src.logger import logger
  from src.search_space.core.model_params import ModelMicroCfg, ModelMacroCfg
  from src.search_space.core.space import SpaceWrapper
  from src.search_space.mlp_api.model_params import MlpMacroCfg
  from src.query_api.interface import profile_NK_trade_off
  from src.query_api.query_api_mlp import GTMLP
  from singa import layer
  from singa import model
  from singa import tensor
  from singa import opt
  from singa import device
  from singa.autograd import Operator
  from singa.layer import Layer
  from singa import singa_wrap
  import argparse
  import numpy
  from torch.utils.data import DataLoader
label_desc: Use image node_id 17 for calling the MlpSpace obj's underlying member method code with example usage: obj.mutate_architecture(parent_arch) and returns: str,  new_model