code: def _get_unscaled(self, slicer):
      indices = self._slice_indices
      if slicer == ():
          with ImageOpener(self.file_like) as fileobj:
              rec_data = array_from_file(
                  self._rec_shape, self._dtype, fileobj, mmap=self._mmap
              )
              rec_data = rec_data[..., indices]
              return rec_data.reshape(self._shape, order="F")
      elif indices[0] != 0 or np.any(np.diff(indices) != 1):
          # We can't load direct from REC file, use inefficient slicing
          return self._get_unscaled(())[slicer]
  
      # Slices all sequential from zero, can use fileslice
      # This gives more efficient volume by volume loading, for example
      with ImageOpener(self.file_like) as fileobj:
          return fileslice(
              fileobj, slicer, self._shape, self._dtype, 0, "F"
          )
  
imports: import re
  import warnings
  from collections import OrderedDict
  from copy import deepcopy
  from io import StringIO
  from locale import getpreferredencoding
  import numpy
  from .affines import apply_affine, dot_reduce, from_matvec
  from .eulerangles import euler2mat
  from .fileslice import fileslice, strided_scalar
  from .nifti1 import unit_codes
  from .openers import ImageOpener
  from .spatialimages import SpatialHeader, SpatialImage
  from .volumeutils import Recoder, array_from_file
label_desc: Use image node_id 6 for calling the PARRECArrayProxy obj's underlying member method code with example usage: obj._get_unscaled(slicer) and returns: fileslice, rec_data, self