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: At each iteration ``application_driver`` calls::
  
      output = tf.session.run(variables_to_eval, feed_dict=data_dict)
  
  to evaluate TF graph elements, where
  ``variables_to_eval`` and ``data_dict`` are retrieved from
  ``iteration_message.ops_to_run`` and
  ``iteration_message.data_feed_dict``
   (In addition to the variables collected by self.output_collector).
  
  The output of `tf.session.run(...)` will be stored at
  ``iteration_message.current_iter_output``, and can be accessed
  from ``engine.handler_network_output.OutputInterpreter``.
  
  override this function for more complex operations
  (such as learning rate decay) according to
  ``iteration_message.current_iter``.
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code: def set_iteration_update(self, iteration_message):
      """
      At each iteration ``application_driver`` calls::
  
          output = tf.session.run(variables_to_eval, feed_dict=data_dict)
  
      to evaluate TF graph elements, where
      ``variables_to_eval`` and ``data_dict`` are retrieved from
      ``iteration_message.ops_to_run`` and
      ``iteration_message.data_feed_dict``
       (In addition to the variables collected by self.output_collector).
  
      The output of `tf.session.run(...)` will be stored at
      ``iteration_message.current_iter_output``, and can be accessed
      from ``engine.handler_network_output.OutputInterpreter``.
  
      override this function for more complex operations
      (such as learning rate decay) according to
      ``iteration_message.current_iter``.
      """
      if iteration_message.is_training:
          iteration_message.data_feed_dict[self.is_validation] = False
      elif iteration_message.is_validation:
          iteration_message.data_feed_dict[self.is_validation] = True
  
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 7 for calling the BaseApplication obj's underlying member method code with example usage: obj.set_iteration_update(iteration_message) without return types