code: def __init__(self, num_classes=1000, filter_size=1):
      super(AlexNetNMP, self).__init__()
      self.features = nn.Sequential(
          nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
          nn.ReLU(inplace=True),
          BlurPool(
              64,
              filt_size=filter_size,
              stride=2,
              pad_off=-1,
              hidden=True,
          ),
          nn.Conv2d(64, 192, kernel_size=5, padding=2),
          nn.ReLU(inplace=True),
          BlurPool(
              192,
              filt_size=filter_size,
              stride=2,
              pad_off=-1,
              hidden=True,
          ),
          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),
          BlurPool(
              256,
              filt_size=filter_size,
              stride=2,
              pad_off=-1,
              hidden=True,
          ),
      )
      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 AlexNetNMP object from inherited base classes: nn with example: obj = AlexNetNMP(num_classes, filter_size)