code: def __init__(self, num_classes=1000):
      super(AlexNet, self).__init__()
      self.features = nn.Sequential(
          nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),
          nn.Conv2d(64, 192, kernel_size=5, padding=2),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),
          nn.Conv2d(192, 384, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(384, 256, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(256, 256, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),
      )
      self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
      self.classifier = nn.Sequential(
          nn.Dropout(),
          nn.Linear(256 * 6 * 6, 4096),
          nn.ReLU(inplace=True),
          nn.Dropout(),
          nn.Linear(4096, 4096),
      )
      self.last_relu = nn.ReLU(inplace=True)
      self.last_relu_fake = FakeReLUM()
      self.last_layer = nn.Linear(4096, num_classes)
  
file_path: robustness/robustness/imagenet_models/alexnet.py
imports: import torch.nn
  from torch.hub import load_state_dict_from_url
  from ..tools.custom_modules import FakeReLUM
label_desc: Use image node_id 1 to create a new AlexNet object from inherited base classes: nn with example: obj = AlexNet(num_classes)