code: def get_squeezenet(
      version,
      pretrained=False,
      device=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.
      device : Device, default CPU
          The device 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(f"squeezenet{version}", root=root),
              device=device,
          )
      return net
  
file_path: mxnet/python/mxnet/gluon/model_zoo/vision/squeezenet.py
imports: import os
  from ....device import cpu
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
label_desc: Use image node_id 3 for calling a global function with example usage: get_squeezenet(version, pretrained, device, root) and returns: net