class_docstr: SENet-154 model which integrates SE blocks with a modified ResNeXt architecture.
  
  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:
  
      * `Hu et al., Squeeze-and-Excitation Networks.
        <https://arxiv.org/abs/1709.01507>`_
code: def __init__(self):
      self._load_nnp("SENet-154.nnp", "SENet-154/SENet-154.nnp")
  
file_path: nnabla/python/src/nnabla/models/imagenet/senet.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 SENet object from inherited base classes: ImageNetBase with example: obj = SENet()