class_docstr: 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'.
  classes : int, default 1000
      Number of classification classes.
class_docstr_tok: ["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","'",".","classes",":","int",",","default","1000","Number","of","classification","classes","."]
code: def hybrid_forward(self, F, x):
      x = self.features(x)
      x = self.output(x)
      return x
  
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 2 for calling the SqueezeNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x