code: def encoder_layers(self):
      return [self.layer0, self.layer1, self.layer2, self.layer3]
  
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 2 for calling the SqueezenetEncoder obj's underlying member method code with example usage: obj.encoder_layers() and returns: list