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","."]
docstr: For all output to be saved as csv, loop through the dictionary of
  output and create the csv
  :return:
docstr_tok: ["For","all","output","to","be","saved","as","csv",",","loop","through","the","dictionary","of","output","and","create","the","csv",":","return",":"]
code: def _save_current_csv(self):
      """
      For all output to be saved as csv, loop through the dictionary of
      output and create the csv
      :return:
      """
      if self.input_image is None:
          return
      subject_name = self.reader.get_subject_id(self.image_id)
      for i in self.csv_out:
          filename = "{}_{}_{}.csv".format(
              i, subject_name, self.postfix
          )
          misc_io.save_csv_array(
              self.output_path, filename, self.csv_out[i]
          )
          self.log_inferred(subject_name, filename)
      return
  
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 6 for calling the GridSamplesAggregator obj's underlying member method code with example usage: obj._save_current_csv() without return types