code: def interpret_output(self, batch_output):
      if self.is_training:
          return True
      return self.output_decoder.decode_batch(
          {"window_image": batch_output["image"]},
          batch_output["location"],
      )
  
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.application.base_application import BaseApplication
  from niftynet.engine.application_factory import ApplicationNetFactory
  from niftynet.engine.application_factory import OptimiserFactory
  from niftynet.engine.application_variables import CONSOLE, NETWORK_OUTPUT, TF_SUMMARIES
  from niftynet.engine.sampler_random_vector_v2 import RandomVectorSampler
  from niftynet.engine.sampler_resize_v2 import ResizeSampler
  from niftynet.contrib.csv_reader.sampler_resize_v2_csv import ResizeSamplerCSV
  from niftynet.engine.windows_aggregator_identity import WindowAsImageAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.binary_masking import BinaryMaskingLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.loss_gan import LossFunction
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.rand_flip import RandomFlipLayer
  from niftynet.layer.rand_rotation import RandomRotationLayer
  from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer
label_desc: Use image node_id 6 for calling the GANApplication obj's underlying member method code with example usage: obj.interpret_output(batch_output) and returns: self, True