class_docstr: InceptionV1 (or 'GoogLeNet')
  
  This is a (re?)implementation of InceptionV1 from the "Going deeper 
  with convolutions" paper. Links:
    * Official CVPR paper, requires subscription: https://ieeexplore.ieee.org/document/7298594
    * Author preprint: https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
    * arXiv: https://arxiv.org/abs/1409.4842
  
  The weights were trained at Google and released in an early TensorFlow
  tutorial. It is possible the parameters are the original weights
  (trained in TensorFlow's predecessor), but we haven't been able to
  confirm this.
  
  As far as we can tell, it is exactly the same as the model described in
  the original paper, where as the slim and caffe implementations have
  minor implementation differences (such as eliding extra classification heads).
code: def post_import(self, scope):
      _populate_inception_bottlenecks(scope)
  
file_path: lucid/lucid/modelzoo/other_models/InceptionV1.py
imports: from __future__ import absolute_import, division, print_function
  import tensorflow
  from lucid.modelzoo.vision_base import Model, _layers_from_list_of_dicts
label_desc: Use image node_id 1 for calling the InceptionV1 obj's underlying member method code with example usage: obj.post_import(scope) without return types