code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      self.data_param = data_param
      self.gan_param = task_param
  
      if self.is_training:
          reader_names = ("image", "conditioning")
      elif self.is_inference:
          # in the inference process use `conditioning` input only
          reader_names = ("conditioning",)
      elif self.is_evaluation:
          tf.logging.fatal(
              "Evaluation is not yet supported in this application."
          )
          raise NotImplementedError
      else:
          tf.logging.fatal(
              "Action `%s` not supported. Expected one of %s",
              self.action,
              self.SUPPORTED_PHASES,
          )
          raise ValueError
      try:
          reader_phase = self.action_param.dataset_to_infer
      except AttributeError:
          reader_phase = None
      file_lists = data_partitioner.get_file_lists_by(
          phase=reader_phase, action=self.action
      )
      self.readers = [
          ImageReader(reader_names).initialise(
              data_param, task_param, file_list
          )
          for file_list in file_lists
      ]
  
      # initialise input preprocessing layers
      foreground_masking_layer = (
          BinaryMaskingLayer(
              type_str=self.net_param.foreground_type,
              multimod_fusion=self.net_param.multimod_foreground_type,
              threshold=0.0,
          )
          if self.net_param.normalise_foreground_only
          else None
      )
      mean_var_normaliser = (
          MeanVarNormalisationLayer(
              image_name="image",
              binary_masking_func=foreground_masking_layer,
          )
          if self.net_param.whitening
          else None
      )
      histogram_normaliser = (
          HistogramNormalisationLayer(
              image_name="image",
              modalities=vars(task_param).get("image"),
              model_filename=self.net_param.histogram_ref_file,
              binary_masking_func=foreground_masking_layer,
              norm_type=self.net_param.norm_type,
              cutoff=self.net_param.cutoff,
              name="hist_norm_layer",
          )
          if (
              self.net_param.histogram_ref_file
              and self.net_param.normalisation
          )
          else None
      )
  
      normalisation_layers = []
      if histogram_normaliser is not None:
          normalisation_layers.append(histogram_normaliser)
      if mean_var_normaliser is not None:
          normalisation_layers.append(mean_var_normaliser)
  
      # initialise training data augmentation layers
      augmentation_layers = []
      if self.is_training:
          if self.action_param.random_flipping_axes != -1:
              augmentation_layers.append(
                  RandomFlipLayer(
                      flip_axes=self.action_param.random_flipping_axes
                  )
              )
          if self.action_param.scaling_percentage:
              augmentation_layers.append(
                  RandomSpatialScalingLayer(
                      min_percentage=self.action_param.scaling_percentage[
                          0
                      ],
                      max_percentage=self.action_param.scaling_percentage[
                          1
                      ],
                      antialiasing=self.action_param.antialiasing,
                      isotropic=self.action_param.isotropic_scaling,
                  )
              )
          if self.action_param.rotation_angle:
              augmentation_layers.append(RandomRotationLayer())
              augmentation_layers[-1].init_uniform_angle(
                  self.action_param.rotation_angle
              )
  
      # only add augmentation to first reader (not validation reader)
      self.readers[0].add_preprocessing_layers(
          normalisation_layers + augmentation_layers
      )
  
      for reader in self.readers[1:]:
          reader.add_preprocessing_layers(normalisation_layers)
  
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 2 for calling the GANApplication obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types