code: def _make_layer(self, block, planes, num_blocks, stride):
      strides = [stride] + [1] * (num_blocks - 1)
      layers = []
      for stride in strides:
          layers.append(block(self.in_planes, planes, stride))
          self.in_planes = planes * block.expansion
      return nn.Sequential(*layers)
  
imports: from PIL import Image
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.transforms
  from torch.utils.data import Dataset, DataLoader
  from torch.optim.lr_scheduler import MultiStepLR
  from art.estimators.classification import PyTorchClassifier
  from art.data_generators import PyTorchDataGenerator
  from art.defences.trainer import AdversarialTrainerAWPPyTorch
  from art.utils import load_cifar10
  from art.attacks.evasion import ProjectedGradientDescent
label_desc: Use image node_id 2 for calling the PreActResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, num_blocks, stride) and returns: nn