code: def get_squeezenet(
      version,
      pretrained=False,
      ctx=cpu(),
      root="~/.mxnet/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 or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      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, tag=pretrained, root=root
              ),
              ctx=ctx,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      return net
  
file_path: gluon-cv/gluoncv/model_zoo/squeezenet.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.contrib.nn import HybridConcurrent
label_desc: Use image node_id 3 for calling a global function with example usage: get_squeezenet(version, pretrained, ctx, root) and returns: net