code: def squeezenet1_1(num_classes=1000, pretrained="imagenet"):
      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.
      """
      model = models.squeezenet1_1(pretrained=False)
      if pretrained is not None:
          settings = pretrained_settings["squeezenet1_1"][pretrained]
          model = load_pretrained(model, num_classes, settings)
      model = modify_squeezenets(model)
      return model
  
imports: from __future__ import print_function, division, absolute_import
  import torchvision.models
  import torch.utils.model_zoo
  import torch.nn.functional
  import types
  import re
label_desc: Use image node_id 19 for calling a global function with example usage: squeezenet1_1(num_classes, pretrained) and returns: model