code: def squeezenet1_0(num_classes=1000, pretrained="imagenet"):
      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.
      """
      model = models.squeezenet1_0(pretrained=False)
      if pretrained is not None:
          settings = pretrained_settings["squeezenet1_0"][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 18 for calling a global function with example usage: squeezenet1_0(num_classes, pretrained) and returns: model