code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      RegressionApplication.initialise_dataset_loader(
          self, data_param, task_param, data_partitioner
      )
      if self.is_training:
          return
      if not task_param.error_map:
          # use the regression application implementation
          return
  
      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
      )
      # modifying the original readers in regression application
      # as we need ground truth labels to generate error maps
      self.readers = [
          ImageReader(["image", "output"]).initialise(
              data_param, task_param, file_list
          )
          for file_list in file_lists
      ]
  
      mean_var_normaliser = MeanVarNormalisationLayer(
          image_name="image"
      )
      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,
              norm_type=self.net_param.norm_type,
              cutoff=self.net_param.cutoff,
              name="hist_norm_layer",
          )
  
      preprocessors = []
      if self.net_param.normalisation:
          preprocessors.append(histogram_normaliser)
      if self.net_param.whitening:
          preprocessors.append(mean_var_normaliser)
      if self.net_param.volume_padding_size:
          preprocessors.append(
              PadLayer(
                  image_name=SUPPORTED_INPUT,
                  border=self.net_param.volume_padding_size,
              )
          )
      self.readers[0].add_preprocessing_layers(preprocessors)
  
file_path: NiftyNet/niftynet/contrib/regression_weighted_sampler/isample_regression.py
imports: import os
  import tensorflow
  from niftynet.application.regression_application import RegressionApplication, SUPPORTED_INPUT
  from niftynet.engine.sampler_uniform_v2 import UniformSampler
  from niftynet.engine.sampler_weighted_v2 import WeightedSampler
  from niftynet.engine.application_variables import NETWORK_OUTPUT
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.pad import PadLayer
label_desc: Use image node_id 1 for calling the ISampleRegression obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types