code: def preprocess(self, data_group):
      for file_idx, filename in enumerate(data_group.preprocessed_case):
          transform_filename = self.generate_output_filename(
              filename, self.transform_string, file_extension=".tfm"
          )
  
          if self.reference_channel is not None:
              if self.reference_channel == file_idx:
                  self.output_filenames[file_idx] = filename
                  self.output_transforms += [None]
                  continue
              else:
                  specific_command = self.command + [
                      "--fixedVolume",
                      quotes(
                          data_group.preprocessed_case[
                              self.reference_channel
                          ]
                      ),
                      "--transformType",
                      self.transform_type,
                      "--initializeTransformMode",
                      self.transform_initialization,
                      "--interpolationMode",
                      self.interpolation,
                      "--samplingPercentage",
                      str(self.sampling_percentage),
                      "--movingVolume",
                      quotes(filename),
                      "--outputVolume",
                      quotes(self.output_filenames[file_idx]),
                      "--outputTransform",
                      quotes(transform_filename),
                  ]
          else:
              specific_command = self.command + [
                  "--fixedVolume",
                  quotes(self.reference_file),
                  "--transformType",
                  self.transform_type,
                  "--initializeTransformMode",
                  self.transform_initialization,
                  "--interpolationMode",
                  self.interpolation,
                  "--samplingPercentage",
                  str(self.sampling_percentage),
                  "--movingVolume",
                  quotes(self.base_file),
                  "--outputVolume",
                  quotes(self.output_filename),
                  "--outputTransform",
                  quotes(transform_filename),
              ]
  
          subprocess.call(
              " ".join(specific_command),
              shell=True,
              stdout=FNULL,
              stderr=subprocess.STDOUT,
          )
  
          # self.output_transforms += [read_image_files(transform_filename)]
  
          if not self.save_transforms:
              os.remove(transform_filename)
  
      self.output_data = self.output_filenames
      data_group.preprocessed_case = self.output_filenames
  
imports: import subprocess
  import os
  import numpy
  import itertools
  from deepneuro.preprocessing.preprocessor import Preprocessor
  from deepneuro.utilities.util import add_parameter, quotes
label_desc: Use image node_id 3 for calling the Coregister obj's underlying member method code with example usage: obj.preprocess(data_group) without return types