code: def initialise_balanced_sampler(self):
      self.sampler = [
          [
              BalancedSampler(
                  reader=reader,
                  window_sizes=self.data_param,
                  batch_size=self.net_param.batch_size,
                  windows_per_image=self.action_param.sample_per_volume,
                  queue_length=self.net_param.queue_length,
              )
              for reader in self.readers
          ]
      ]
  
imports: import tensorflow
  import copy
  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
  from niftynet.engine.application_variables import NETWORK_OUTPUT
  from niftynet.engine.application_variables import TF_SUMMARIES
  from niftynet.engine.sampler_grid_v2 import GridSampler
  from niftynet.engine.sampler_resize_v2 import ResizeSampler
  from niftynet.engine.sampler_uniform_v2 import UniformSampler
  from niftynet.engine.sampler_weighted_v2 import WeightedSampler
  from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
  from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.crop import CropLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.loss_regression import LossFunction
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.pad import PadLayer
  from niftynet.layer.post_processing import PostProcessingLayer
  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 7 for calling the RegressionRecApplication obj's underlying member method code with example usage: obj.initialise_balanced_sampler() without return types