class_docstr: Collect all tf.Tensor object, to be evaluated by tf.Session.run()
  These objects are grouped into::
  
      NETWORK_OUTPUT: to be decoded by an aggregator
      CONSOLE: to be printed on command line
      TF_SUMMARIES: to be added to tensorboard visualisation
docstr: add a variable to be displayed in tensorboard
docstr_tok: ["add","a","variable","to","be","displayed","in","tensorboard"]
code: def _add_to_tf_summary(
      self, var, name, average_over_devices=False, summary_type="scalar"
  ):
      """
      add a variable to be displayed in tensorboard
      """
      name = self._add_to_dict(
          self.summary_vars, var, name, average_over_devices
      )
      # _add_to_dict might change the name parameter to avoid
      # naming clash, here uses the returned new name for summary
      values = self.summary_vars.get(name, None)
      if isinstance(values, tf.Tensor):
          summary_op = util.look_up_operations(
              summary_type, SUPPORTED_SUMMARY
          )
          summary_op(name, values, collections=[TF_SUMMARIES])
  
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from tensorflow.contrib.framework import list_variables
  from niftynet.io.misc_io import image3_axial, image3_coronal, image3_sagittal, resolve_checkpoint
  from niftynet.utilities import util_common
  from niftynet.utilities.restore_initializer import restore_initializer
label_desc: Use image node_id 8 for calling the OutputsCollector obj's underlying member method code with example usage: obj._add_to_tf_summary(var, name, average_over_devices, summary_type) without return types