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: Reshapes layer activations as needed to feed through the model network.
  
  Override this for models that require reshaping of the activations for use
  in TCAV.
  
  Args:
    layer_acts: Activations as returned by run_examples.
  
  Returns:
    Activations in model-dependent form; the default is a squeezed array (i.e.
    at most one dimensions of size 1).
code: def reshape_activations(self, layer_acts):
      """Reshapes layer activations as needed to feed through the model network.
  
      Override this for models that require reshaping of the activations for use
      in TCAV.
  
      Args:
        layer_acts: Activations as returned by run_examples.
  
      Returns:
        Activations in model-dependent form; the default is a squeezed array (i.e.
        at most one dimensions of size 1).
      """
      return np.asarray(layer_acts).squeeze()
  
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 8 for calling the ModelWrapper obj's underlying member method code with example usage: obj.reshape_activations(layer_acts) and returns: np