class_docstr: Simple wrapper of the for models with session object for TCAV.
  
  Supports easy inference with no need to deal with the feed_dicts.
class_docstr_tok: ["Simple","wrapper","of","the","for","models","with","session","object","for","TCAV",".","Supports","easy","inference","with","no","need","to","deal","with","the","feed_dicts","."]
docstr: Makes gradient tensors for all bottleneck tensors.
code: def _make_gradient_tensors(self):
      """Makes gradient tensors for all bottleneck tensors."""
  
      self.bottlenecks_gradients = {}
      for bn in self.bottlenecks_tensors:
          self.bottlenecks_gradients[bn] = tf.gradients(
              ys=self.loss, xs=self.bottlenecks_tensors[bn]
          )[0]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from abc import ABCMeta
  from abc import abstractmethod
  from six.moves import zip
  import numpy
  import six
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 4 for calling the ModelWrapper obj's underlying member method code with example usage: obj._make_gradient_tensors() without return types