code: def squeezenet1_0(pretrained=False, progress=True, **kwargs):
      r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
      accuracy with 50x fewer parameters and <0.5MB model size"
      <https://arxiv.org/abs/1602.07360>`_ paper.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _squeezenet("1_0", pretrained, progress, **kwargs)
  
file_path: robustness/robustness/imagenet_models/squeezenet.py
imports: import torch
  import torch.nn
  import torch.nn.init
  from torch.hub import load_state_dict_from_url
  from ..tools.custom_modules import FakeReLUM
label_desc: Use image node_id 2 for calling a global function with example usage: squeezenet1_0(pretrained, progress) and returns: _squeezenet