code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      self.data_param = data_param
      self.segmentation_param = task_param
  
      # initialise input image readers
      if self.is_training:
          reader_names = ("image", "label", "weight", "sampler")
      elif self.is_inference:
          # in the inference process use `image` input only
          reader_names = ("image",)
      elif self.is_evaluation:
          reader_names = ("image", "label", "inferred")
      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
      ]
  
      foreground_masking_layer = None
      if self.net_param.normalise_foreground_only:
          foreground_masking_layer = BinaryMaskingLayer(
              type_str=self.net_param.foreground_type,
              multimod_fusion=self.net_param.multimod_foreground_type,
              threshold=0.0,
          )
  
      mean_var_normaliser = MeanVarNormalisationLayer(
          image_name="image",
          binary_masking_func=foreground_masking_layer,
      )
      histogram_normaliser = None
      if self.net_param.histogram_ref_file:
          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",
          )
  
      label_normaliser = None
      if self.net_param.histogram_ref_file:
          label_normaliser = DiscreteLabelNormalisationLayer(
              image_name="label",
              modalities=vars(task_param).get("label"),
              model_filename=self.net_param.histogram_ref_file,
          )
  
      normalisation_layers = []
      if self.net_param.normalisation:
          normalisation_layers.append(histogram_normaliser)
      if self.net_param.whitening:
          normalisation_layers.append(mean_var_normaliser)
      if task_param.label_normalisation and (
          self.is_training or not task_param.output_prob
      ):
          normalisation_layers.append(label_normaliser)
  
      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
                      ],
                  )
              )
          if (
              self.action_param.rotation_angle
              or self.action_param.rotation_angle_x
              or self.action_param.rotation_angle_y
              or self.action_param.rotation_angle_z
          ):
              rotation_layer = RandomRotationLayer()
              if self.action_param.rotation_angle:
                  rotation_layer.init_uniform_angle(
                      self.action_param.rotation_angle
                  )
              else:
                  rotation_layer.init_non_uniform_angle(
                      self.action_param.rotation_angle_x,
                      self.action_param.rotation_angle_y,
                      self.action_param.rotation_angle_z,
                  )
              augmentation_layers.append(rotation_layer)
          if self.action_param.bias_field_range:
              bias_field_layer = RandomBiasFieldLayer()
              bias_field_layer.init_order(self.action_param.bf_order)
              bias_field_layer.init_uniform_coeff(
                  self.action_param.bias_field_range
              )
              augmentation_layers.append(bias_field_layer)
  
      volume_padding_layer = [
          PadLayer(
              image_name=SUPPORTED_INPUT,
              border=self.net_param.volume_padding_size,
              mode=self.net_param.volume_padding_mode,
              pad_to=self.net_param.volume_padding_to_size,
          )
      ]
  
      self.readers[0].add_preprocessing_layers(
          volume_padding_layer
          + normalisation_layers
          + augmentation_layers
      )
  
      for reader in self.readers[1:]:
          reader.add_preprocessing_layers(
              volume_padding_layer + normalisation_layers
          )
  
file_path: NiftyNet/niftynet/contrib/segmentation_bf_aug/segmentation_application_bfaug.py
imports: import tensorflow
  from niftynet.application.segmentation_application import SegmentationApplication, SUPPORTED_INPUT
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.binary_masking import BinaryMaskingLayer
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.pad import PadLayer
  from niftynet.layer.rand_bias_field import RandomBiasFieldLayer
  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 SegmentationApplicationBFAug obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types