class_docstr: Image windows are retrieved and analysed by the
  tensorflow graph, this windows aggregator receives
  output window data in numpy array. To access image-level
  information the reader is needed.
class_docstr_tok: ["Image","windows","are","retrieved","and","analysed","by","the","tensorflow","graph",",","this","windows","aggregator","receives","output","window","data","in","numpy","array",".","To","access","image-level","information","the","reader","is","needed","."]
docstr: The implementation of caching and writing batch output
  goes here. This function should return False when the
  location vector is stopping signal, to notify the
  inference loop to terminate.
  
  :param args:
  :param kwargs:
  :return: True if more batch data are expected, False otherwise
docstr_tok: ["The","implementation","of","caching","and","writing","batch","output","goes","here",".","This","function","should","return","False","when","the","location","vector","is","stopping","signal",",","to","notify","the","inference","loop","to","terminate",".",":","param","args",":",":","param","kwargs",":",":","return",":","True","if","more","batch","data","are","expected",",","False","otherwise"]
code: def decode_batch(self, *args, **kwargs):
      """
      The implementation of caching and writing batch output
      goes here. This function should return False when the
      location vector is stopping signal, to notify the
      inference loop to terminate.
  
      :param args:
      :param kwargs:
      :return: True if more batch data are expected, False otherwise
      """
      raise NotImplementedError
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_base.py
imports: from __future__ import absolute_import, print_function, division
  import os
  import numpy
  import tensorflow
  from niftynet.engine.image_window import N_SPATIAL
label_desc: Use image node_id 5 for calling the ImageWindowsAggregator obj's underlying member method code with example usage: obj.decode_batch() without return types