code: def get_squeezenet(version, pretrained=False, ctx=cpu(),
                     root=os.path.join(base.data_dir(), 'models'), **kwargs):
      r
      net = SqueezeNet(version, **kwargs)
      if pretrained:
          from ..model_store import get_model_file
          net.load_parameters(get_model_file('squeezenet%s'%version, root=root), ctx=ctx)
      return net
docs: r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
      and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper.
      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.
  
      Parameters
      ----------
      version : str
          Version of squeezenet. Options are '1.0', '1.1'.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default $MXNET_HOME/models
          Location for keeping the model parameters.