code: def __init__(
      self,
      num_classes=1000,
      filter_size=4,
      pool_only=False,
      relu_first=True,
  ):
      super(AlexNet, self).__init__()
  
      if (
          pool_only
      ):  # only apply LPF to pooling layers, so run conv1 at stride 4 as before
          first_ds = [
              nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
          ]
      else:
          if relu_first:  # this is the right order
              first_ds = [
                  nn.Conv2d(3, 64, kernel_size=11, stride=2, padding=2),
                  nn.ReLU(inplace=True),
                  BlurPool(64, filt_size=filter_size, stride=2),
              ]
          else:  # this is the wrong order, since it's equivalent to downsampling the image first
              first_ds = [
                  nn.Conv2d(3, 64, kernel_size=11, stride=2, padding=2),
                  BlurPool(64, filt_size=filter_size, stride=2),
                  nn.ReLU(inplace=True),
              ]
  
      first_ds += [
          nn.MaxPool2d(kernel_size=3, stride=1),
          BlurPool(64, filt_size=filter_size, stride=2),
          nn.Conv2d(64, 192, kernel_size=5, padding=2),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=1),
          BlurPool(192, filt_size=filter_size, 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=1),
          BlurPool(256, filt_size=filter_size, stride=2),
      ]
      self.features = nn.Sequential(*first_ds)
  
      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),
          nn.ReLU(inplace=True),
          nn.Linear(4096, num_classes),
      )
  
file_path: antialiased-cnns/antialiased_cnns/alexnet.py
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
  import numpy
  from antialiased_cnns import *
label_desc: Use image node_id 1 to create a new AlexNet object from inherited base classes: nn with example: obj = AlexNet(num_classes, filter_size, pool_only, relu_first)