class_docstr: AlexNet model.
  
  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 pooling layer.
  * ``'lastconv'``: The input of the final pooling layer without ReLU activation.
  * ``'lastconv+relu'``: Network up to ``'lastconv'`` followed by ReLU activation.
  * ``'lastfeature'``: Network up to one layer before ``'classifier'``, but without activation.
  
  References:
      * `Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton: ImageNet Classification with Deep ConvolutionalNeural Networks.
        <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_
code: def __call__(
      self,
      input_var=None,
      use_from=None,
      use_up_to="classifier",
      training=False,
      force_global_pooling=False,
      check_global_pooling=True,
      returns_net=False,
      verbose=0,
      with_aux_tower=False,
  ):
      input_var = self.get_input_var(input_var)
  
      callback = NnpNetworkPass(verbose)
      callback.remove_and_rewire("ImageAugmentationX")
      callback.set_variable("TrainingInput", input_var)
      self.use_up_to(use_up_to, callback)
      if not training:
          callback.remove_and_rewire("TrainNet/Dropout")
          callback.remove_and_rewire("TrainNet/Dropout_2")
          callback.fix_parameters()
      batch_size = input_var.shape[0]
      net = self.nnp.get_network(
          "Training", batch_size=batch_size, callback=callback
      )
      if returns_net:
          return net
      else:
          return list(net.outputs.values())[0]
  
file_path: nnabla/python/src/nnabla/models/imagenet/alexnet.py
imports: from __future__ import absolute_import
  from nnabla.utils.nnp_graph import NnpNetworkPass
  from .base import ImageNetBase
label_desc: Use image node_id 3 for calling the AlexNet obj's underlying member method code with example usage: obj.__call__(input_var, use_from, use_up_to, training, force_global_pooling, check_global_pooling, returns_net, verbose, with_aux_tower) and returns: net, list