class_docstr: AlexNet backbone.
  
  Args:
      num_classes (int): number of classes for classification.
code: def __init__(self, num_classes=-1):
      super(AlexNet, self).__init__()
      self.num_classes = num_classes
      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),
      )
      if self.num_classes > 0:
          self.classifier = nn.Sequential(
              nn.Dropout(),
              nn.Linear(256 * 6 * 6, 4096),
              nn.ReLU(inplace=True),
              nn.Dropout(),
              nn.Linear(4096, 4096),
              nn.ReLU(inplace=True),
              nn.Linear(4096, num_classes),
          )
  
imports: import logging
  import torch.nn
label_desc: Use image node_id 1 to create a new AlexNet object from inherited base classes: nn with example: obj = AlexNet(num_classes)