class_docstr: 5D image from a single file,
  resampling and reorientation are implemented as
  operations on each 3D slice individually.
  
  (5D image from a set of 4D files is currently not supported)
class_docstr_tok: ["5D","image","from","a","single","file",",","resampling","and","reorientation","are","implemented","as","operations","on","each","3D","slice","individually",".","(","5D","image","from","a","set","of","4D","files","is","currently","not","supported",")"]
code: def __init__(
      self,
      file_path,
      name,
      interp_order,
      output_pixdim,
      output_axcodes,
      loader,
  ):
      SpatialImage4D.__init__(
          self,
          file_path=file_path,
          name=name,
          interp_order=interp_order,
          output_pixdim=output_pixdim,
          output_axcodes=output_axcodes,
          loader=loader,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from abc import ABCMeta, abstractmethod
  import nibabel
  import numpy
  import tensorflow
  from six import with_metaclass, string_types
  import niftynet.io.misc_io
  from niftynet.io.image_loader import load_image_obj
  from niftynet.io.misc_io import resolve_file_name, dtype_casting
  from niftynet.utilities.niftynet_global_config import NiftyNetGlobalConfig
label_desc: Use image node_id 1 to create a new SpatialImage5D object from inherited base classes: SpatialImage4D with example: obj = SpatialImage5D(file_path, name, interp_order, output_pixdim, output_axcodes, loader)