code: def squeezenet1_1(pretrained=False, progress=True, **kwargs):
      r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
      <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
      SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
      than SqueezeNet 1.0, without sacrificing accuracy.
  
      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_1", 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 3 for calling a global function with example usage: squeezenet1_1(pretrained, progress) and returns: _squeezenet