code: def __init__(self, pretrained=True, layers=[1, 2, 3]):
      super().__init__([64, 128, 256, 512], [4, 8, 16, 16], layers)
      squeezenet = squeezenet1_1(pretrained=pretrained)
  
      # nn.Conv2d(3, 64, kernel_size=3, stride=2),
      # nn.ReLU(inplace=True),
      # nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
      self.layer0 = nn.Sequential(
          OrderedDict(
              [
                  ("conv1", squeezenet.features[0]),
                  ("relu1", nn.ReLU(inplace=True)),
                  (
                      "pool1",
                      nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
                  ),
              ]
          )
      )
  
      # Fire(64, 16, 64, 64),
      # Fire(128, 16, 64, 64),
      # nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
      self.layer1 = nn.Sequential(
          squeezenet.features[3],
          squeezenet.features[4],
          # squeezenet.features[5],
          nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
      )
  
      # Fire(128, 32, 128, 128),
      # Fire(256, 32, 128, 128),
      # nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
      self.layer2 = nn.Sequential(
          squeezenet.features[6],
          squeezenet.features[7],
          # squeezenet.features[8],
          nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
      )
  
      # Fire(256, 48, 192, 192),
      # Fire(384, 48, 192, 192),
      # Fire(384, 64, 256, 256),
      # Fire(512, 64, 256, 256),
      self.layer3 = nn.Sequential(
          squeezenet.features[9],
          squeezenet.features[10],
          squeezenet.features[11],
          squeezenet.features[12],
      )
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/encoders/squeezenet.py
imports: from collections import OrderedDict
  from torch import nn
  from torchvision.models import squeezenet1_1
  from .common import EncoderModule, make_n_channel_input
label_desc: Use image node_id 1 to create a new SqueezenetEncoder object from inherited base classes: EncoderModule with example: obj = SqueezenetEncoder(pretrained, layers)