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: Find tensors from the graph by their names.
  
  Depending on how the model is loaded, tensors in the graph
  may or may not have 'import/' prefix added to every tensor name.
  This is true even if the tensors already have 'import/' prefix.
  The 'ends' and 'bottlenecks_tensors' dictionary should map to tensors
  with the according name.
code: def _find_ends_and_bottleneck_tensors(self, node_dict):
      """Find tensors from the graph by their names.
  
      Depending on how the model is loaded, tensors in the graph
      may or may not have 'import/' prefix added to every tensor name.
      This is true even if the tensors already have 'import/' prefix.
      The 'ends' and 'bottlenecks_tensors' dictionary should map to tensors
      with the according name.
      """
      self.bottlenecks_tensors = {}
      self.ends = {}
      for k, v in six.iteritems(node_dict):
          if self.import_prefix:
              v = "import/" + v
          tensor = self.sess.graph.get_operation_by_name(
              v.strip(":0")
          ).outputs[0]
          if k == "input" or k == "prediction":
              self.ends[k] = tensor
          else:
              self.bottlenecks_tensors[k] = tensor
  
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 3 for calling the ModelWrapper obj's underlying member method code with example usage: obj._find_ends_and_bottleneck_tensors(node_dict) without return types