class_docstr: BaseApplication represents an interface.
  
  Each application ``type_str`` should support to use
  the standard training and inference driver.
class_docstr_tok: ["BaseApplication","represents","an","interface",".","Each","application","``","type_str","``","should","support","to","use","the","standard","training","and","inference","driver","."]
docstr: Implement output interpretations, e.g., save to hard drive
  cache output windows.
  
  :param batch_output: outputs by running the tf graph
  :return: True indicates the driver should continue the loop
      False indicates the drive should stop
docstr_tok: ["Implement","output","interpretations",",","e.g.",",","save","to","hard","drive","cache","output","windows",".",":","param","batch_output",":","outputs","by","running","the","tf","graph",":","return",":","True","indicates","the","driver","should","continue","the","loop","False","indicates","the","drive","should","stop"]
code: def interpret_output(self, batch_output):
      """
      Implement output interpretations, e.g., save to hard drive
      cache output windows.
  
      :param batch_output: outputs by running the tf graph
      :return: True indicates the driver should continue the loop
          False indicates the drive should stop
      """
      raise NotImplementedError
  
imports: import os
  from argparse import Namespace
  import tensorflow
  from six import with_metaclass
  from niftynet.engine.signal import TRAIN, INFER, EVAL
label_desc: Use image node_id 5 for calling the BaseApplication obj's underlying member method code with example usage: obj.interpret_output(batch_output) without return types