code: def initialise_uniform_sampler(self):
      self.sampler = [
          [
              UniformSampler(
                  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: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.application.base_application import BaseApplication
  from niftynet.engine.application_factory import ApplicationNetFactory, InitializerFactory, OptimiserFactory
  from niftynet.io.image_reader import ImageReader
  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.sampler_balanced_v2 import BalancedSampler
  from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
  from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
  from niftynet.engine.windows_aggregator_identity import WindowAsImageAggregator
  from niftynet.layer.loss_segmentation import LossFunction
  from niftynet.layer.post_processing import PostProcessingLayer
  from niftynet.engine.application_variables import CONSOLE, TF_SUMMARIES, NETWORK_OUTPUT
label_desc: Use image node_id 3 for calling the MultiOutputApplication obj's underlying member method code with example usage: obj.initialise_uniform_sampler() without return types