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: Get the corresponding input image of these batch data.
  So that the batch data can be stored correctly in
  terms of interpolation order, orientation, pixdims.
  
  :return: an image object from image reader
docstr_tok: ["Get","the","corresponding","input","image","of","these","batch","data",".","So","that","the","batch","data","can","be","stored","correctly","in","terms","of","interpolation","order",",","orientation",",","pixdims",".",":","return",":","an","image","object","from","image","reader"]
code: def input_image(self):
      """
      Get the corresponding input image of these batch data.
      So that the batch data can be stored correctly in
      terms of interpolation order, orientation, pixdims.
  
      :return: an image object from image reader
      """
      if self.image_id is not None and self.reader:
          return self.reader.output_list[self.image_id]
      return None
  
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 2 for calling the ImageWindowsAggregator obj's underlying member method code with example usage: obj.input_image() and returns: None, self