class_docstr: SqueezeNet model for architecture-v1.0 and v1.1 .
  
  Args:
      version (str): Version chosen from 'v1.0' and 'v1.1'.
  
  The following is a list of string that can be specified to ``use_up_to`` option in ``__call__`` method;
  
  * ``'classifier'`` (default): The output of the final affine layer for classification.
  * ``'pool'``: The output of the final global average pooling.
  * ``'lastconv'``: The input of the final global average pooling without ReLU activation.
  * ``'lastconv+relu'``: Network up to ``'lastconv'`` followed by ReLU activation.
  
  References:
  
      * `Iandola, Forrest N. et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.
        <https://arxiv.org/abs/1602.07360>`_
      * `Iandola, Forrest N. et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size.
        <https://arxiv.org/abs/1602.07360v1>`_
      * `DeepScale/SqueezeNet on GitHub <https://github.com/DeepScale/SqueezeNet>`_
code: def __init__(self, version="v1.1"):
      # Check versions
      assert version in (
          "v1.0",
          "v1.1",
      ), "version must be chosen from {'v1.0', 'v1.1'}"
      self.version = version
      self._prefix = ""
      if version == "v1.1":
          self._prefix = "SqueezeNet/"
  
      # Load nnp
      self._load_nnp(
          "SqueezeNet-{}.nnp".format(version[1:]),
          "SqueezeNet-{0}/SqueezeNet-{0}.nnp".format(version[1:]),
      )
  
file_path: nnabla/python/src/nnabla/models/imagenet/squeezenet.py
imports: from __future__ import absolute_import
  from nnabla.utils.nnp_graph import NnpNetworkPass
  from .base import ImageNetBase
label_desc: Use image node_id 1 to create a new SqueezeNet object from inherited base classes: ImageNetBase with example: obj = SqueezeNet(version)