code: def __init__(self, pretrained=False):
      super(SqueezeNet, self).__init__()
  
      self.feat_1 = nn.Sequential(
          nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
          nn.ReLU(inplace=True),
      )
      self.feat_2 = nn.Sequential(
          nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
          Fire(64, 16, 64, 64),
          Fire(128, 16, 64, 64),
      )
      self.feat_3 = nn.Sequential(
          nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
          Fire(128, 32, 128, 128, 2),
          Fire(256, 32, 128, 128, 2),
      )
      self.feat_4 = nn.Sequential(
          Fire(256, 48, 192, 192, 4),
          Fire(384, 48, 192, 192, 4),
          Fire(384, 64, 256, 256, 4),
          Fire(512, 64, 256, 256, 4),
      )
      if pretrained:
          weights = squeezenet1_1(pretrained=True).features.state_dict()
          load_weights_sequential(self, weights)
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
label_desc: Use image node_id 1 to create a new SqueezeNet object from inherited base classes: nn with example: obj = SqueezeNet(pretrained)