code: def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
      r
      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
docs: 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.