code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      self.data_param = data_param
      self.autoencoder_param = task_param
  
      if not self.is_training:
          self._infer_type = look_up_operations(
              self.autoencoder_param.inference_type, SUPPORTED_INFERENCE
          )
      else:
          self._infer_type = None
      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
      )
      # read each line of csv files into an instance of Subject
      if self.is_evaluation:
          NotImplementedError(
              "Evaluation is not yet " "supported in this application."
          )
      if self.is_training:
          self.readers = []
          self.csv_reader = []
          for file_list in file_lists:
              reader = ImageReader(["image"])
              reader.initialise(data_param, task_param, file_list)
              self.readers.append(reader)
      if self._infer_type in ("encode", "encode-decode"):
          self.readers = [ImageReader(["image"])]
          self.readers[0].initialise(
              data_param, task_param, file_lists[0]
          )
      elif self._infer_type == "sample":
          self.readers = []
          self.csv_reader = []
      elif self._infer_type == "linear_interpolation":
          self.csv_reader = []
          self.readers = [ImageReader(["feature"])]
          self.readers[0].initialise(
              data_param, task_param, file_lists[0]
          )
  
imports: 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
  from niftynet.engine.application_variables import NETWORK_OUTPUT
  from niftynet.engine.application_variables import TF_SUMMARIES
  from niftynet.engine.sampler_linear_interpolate_v2 import LinearInterpolateSampler
  from niftynet.contrib.csv_reader.sampler_linear_interpolate_v2_csv import LinearInterpolateSamplerCSV
  from niftynet.engine.sampler_resize_v2 import ResizeSampler
  from niftynet.contrib.csv_reader.sampler_resize_v2_csv import ResizeSamplerCSV
  from niftynet.contrib.csv_reader.csv_reader import CSVReader
  from niftynet.engine.windows_aggregator_identity import WindowAsImageAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.loss_autoencoder import LossFunction
  from niftynet.utilities.util_common import look_up_operations
label_desc: Use image node_id 2 for calling the AutoencoderApplication obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types