parameters: (version, pretrained=False, ctx=cpu(),
                     root=os.path.join(base.data_dir(), 'models'), **kwargs)
return_statement: return net
docstring: 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.
docstring_summary: 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.
docstring_tokens: 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 .
function: def get_squeezenet(version, pretrained=False, ctx=cpu(),
                     root=os.path.join(base.data_dir(), 'models'), **kwargs):
      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.
      """
      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
function_tokens: def get_squeezenet ( version , pretrained = False , ctx = cpu ( ) , root = os . path . join ( base . data_dir ( ) , 'models' ) , * * kwargs ) : 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