code: def __init__(
      self,
      nfield: int,
      nfeat: int,
      nemb: int,
      hidden_layer_list: list,
      dropout_rate: float,
      noutput: int,
      use_bn: bool = True,
  ):
      # def __init__(self, data_size=10, perceptron_size=100, num_classes=10, layer_hidden_list=[10,10,10,10]):
      super(SINGADNNModel, self).__init__()
      # self.num_classes = num_classes
      self.dimension = 2  # data dimension = 2
  
      self.mlp_ninput = nfield * nemb
      self.nfeat = nfeat
  
      layer_hidden_list = []
      for index, layer_size in enumerate(hidden_layer_list):
          layer_hidden_list.append(layer_size)
      self.relu = layer.ReLU()
      self.linear1 = layer.Linear(layer_hidden_list[0])
      # print ("linear1.in_features: ", self.linear1.in_features)
      # print ("linear1.out_features: ", self.linear1.out_features)
      self.linear2 = layer.Linear(layer_hidden_list[1])
      # print ("linear2.in_features: ", self.linear2.in_features)
      # print ("linear2.out_features: ", self.linear2.out_features)
      self.linear3 = layer.Linear(layer_hidden_list[2])
      # print ("linear3.in_features: ", self.linear3.in_features)
      # print ("linear3.out_features: ", self.linear3.out_features)
      self.linear4 = layer.Linear(layer_hidden_list[3])
      # print ("linear4.in_features: ", self.linear4.in_features)
      # print ("linear4.out_features: ", self.linear4.out_features)
      self.linear5 = layer.Linear(noutput)
      # print ("linear5.in_features: ", self.linear5.in_features)
      # print ("linear5.out_features: ", self.linear5.out_features)
      self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
      self.sum_error = SumErrorLayer()
      # for weight-sharing
      self.is_masked_subnet = False
      self.hidden_layer_list = hidden_layer_list
      # Initialize subnet mask with ones
      self.subnet_mask = [np.ones(size) for size in hidden_layer_list]
  
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 1 to create a new SINGADNNModel object from inherited base classes: model with example: obj = SINGADNNModel(nfield, nfeat, nemb, hidden_layer_list, dropout_rate, noutput, use_bn)