code: def __init__(self, num_classes=1000, init_weights=False):
      super(AlexNet, self).__init__()
      self.features = nn.Sequential(
          nn.Conv2d(
              3, 48, kernel_size=11, stride=4, padding=2
          ),  # input[3, 224, 224]  output[48, 55, 55]
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),  # output[48, 27, 27]
          nn.Conv2d(
              48, 128, kernel_size=5, padding=2
          ),  # output[128, 27, 27]
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),  # output[128, 13, 13]
          nn.Conv2d(
              128, 192, kernel_size=3, padding=1
          ),  # output[192, 13, 13]
          nn.ReLU(inplace=True),
          nn.Conv2d(
              192, 192, kernel_size=3, padding=1
          ),  # output[192, 13, 13]
          nn.ReLU(inplace=True),
          nn.Conv2d(
              192, 128, kernel_size=3, padding=1
          ),  # output[128, 13, 13]
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),  # output[128, 6, 6]
      )
      self.classifier = nn.Sequential(
          nn.Dropout(p=0.5),
          nn.Linear(128 * 6 * 6, 2048),
          nn.ReLU(inplace=True),
          nn.Dropout(p=0.5),
          nn.Linear(2048, 2048),
          nn.ReLU(inplace=True),
          nn.Linear(2048, num_classes),
      )
      if init_weights:
          self._initialize_weights()
  
imports: import torch.nn
  import torch
label_desc: Use image node_id 1 to create a new AlexNet object from inherited base classes: nn with example: obj = AlexNet(num_classes, init_weights)