class_docstr: 4D image from a set of 3D volumes,
  supports resampling and reorientation.
  
  The 3D volumes are concatenated in the fifth dim (modality dim)
class_docstr_tok: ["4D","image","from","a","set","of","3D","volumes",",","supports","resampling","and","reorientation",".","The","3D","volumes","are","concatenated","in","the","fifth","dim","(","modality","dim",")"]
code: def __init__(
      self,
      file_path,
      name,
      interp_order,
      output_pixdim,
      output_axcodes,
      loader,
  ):
      SpatialImage3D.__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 SpatialImage4D object from inherited base classes: SpatialImage3D with example: obj = SpatialImage4D(file_path, name, interp_order, output_pixdim, output_axcodes, loader)