class_docstr: This class keeps record of the currently cached image,
  initialised as all zeros, and the values are replaced
  by image window data decoded from batch.
class_docstr_tok: ["This","class","keeps","record","of","the","currently","cached","image",",","initialised","as","all","zeros",",","and","the","values","are","replaced","by","image","window","data","decoded","from","batch","."]
code: def __init__(
      self,
      image_reader,
      name="image",
      output_path=os.path.join(".", "output"),
      window_border=(),
      interp_order=0,
      postfix="niftynet_out",
      fill_constant=0.0,
  ):
      ImageWindowsAggregator.__init__(
          self, image_reader=image_reader, output_path=output_path
      )
      self.name = name
      self.image_out = None
      self.csv_out = None
      self.window_border = window_border
      self.output_interp_order = interp_order
      self.postfix = postfix
      self.fill_constant = fill_constant
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_grid.py
imports: from __future__ import absolute_import, division, print_function
  import os
  from collections import OrderedDict
  import numpy
  import pandas
  import niftynet.io.misc_io
  from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.pad import PadLayer
label_desc: Use image node_id 1 to create a new GridSamplesAggregator object from inherited base classes: ImageWindowsAggregator with example: obj = GridSamplesAggregator(image_reader, name, output_path, window_border, interp_order, postfix, fill_constant)