code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      self.data_param = data_param
      self.registration_param = task_param
  
      if self.is_evaluation:
          NotImplementedError(
              "Evaluation is not yet " "supported in this application."
          )
      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 = []
      for file_list in file_lists:
          fixed_reader = ImageReader({"fixed_image", "fixed_label"})
          fixed_reader.initialise(data_param, task_param, file_list)
          self.readers.append(fixed_reader)
  
          moving_reader = ImageReader({"moving_image", "moving_label"})
          moving_reader.initialise(data_param, task_param, file_list)
          self.readers.append(moving_reader)
  
imports: from __future__ import absolute_import, division, print_function
  import tensorflow
  from niftynet.application.base_application import BaseApplication
  from niftynet.io.image_reader import ImageReader
  from niftynet.contrib.sampler_pairwise.sampler_pairwise_uniform import PairwiseUniformSampler
  from niftynet.contrib.sampler_pairwise.sampler_pairwise_resize import PairwiseResizeSampler
  from niftynet.contrib.csv_reader.csv_reader import CSVReader
  from niftynet.engine.application_factory import OptimiserFactory, ApplicationNetFactory
  from niftynet.engine.application_variables import NETWORK_OUTPUT, CONSOLE, TF_SUMMARIES
  from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
  from niftynet.layer.resampler import ResamplerLayer
  from niftynet.layer.pad import PadLayer
  from niftynet.layer.loss_segmentation import LossFunction
label_desc: Use image node_id 2 for calling the RegApp obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types