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: Return the gradient of the loss with respect to the bottleneck_name.
  
  Args:
    acts: activation of the bottleneck
    y: index of the logit layer
    bottleneck_name: name of the bottleneck to get gradient wrt.
    example: input example. Unused by default. Necessary for getting gradients
      from certain models, such as BERT.
  
  Returns:
    the gradient array.
code: def get_gradient(self, acts, y, bottleneck_name, example):
      """Return the gradient of the loss with respect to the bottleneck_name.
  
      Args:
        acts: activation of the bottleneck
        y: index of the logit layer
        bottleneck_name: name of the bottleneck to get gradient wrt.
        example: input example. Unused by default. Necessary for getting gradients
          from certain models, such as BERT.
  
      Returns:
        the gradient array.
      """
      return self.sess.run(
          self.bottlenecks_gradients[bottleneck_name],
          {
              self.bottlenecks_tensors[bottleneck_name]: acts,
              self.y_input: y,
          },
      )
  
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 5 for calling the ModelWrapper obj's underlying member method code with example usage: obj.get_gradient(acts, y, bottleneck_name, example) and returns: self