code: def get_multiple_3d_targets():
      test_image, input_shape = get_multiple_2d_images()
      test_target = np.array(test_image)
  
      test_target[0] = test_target[0, ::-1]
      test_target[1] = test_target[1, :, ::-1]
      test_target[2] = test_target[2, ::-1, ::-1]
  
      factor = 1.5
      shape = input_shape[:]
      shape[1] = np.floor(input_shape[1] * factor).astype(np.int)
      shape[2] = np.floor(input_shape[2] * factor).astype(np.int)
  
      from scipy.ndimage import zoom
  
      zoomed_target = []
      for img in test_target:
          zoomed_target.append(zoom(img, [factor, factor, 1]))
      test_target = np.stack(zoomed_target, axis=0).astype(np.uint8)
      test_target = np.expand_dims(test_target, axis=1)
      test_target = np.concatenate([test_target, test_target], axis=1)
      return test_target, test_target.shape
  
imports: from __future__ import absolute_import, print_function, division
  import base64
  import numpy
  import tensorflow
  from niftynet.layer.grid_warper import AffineGridWarperLayer
  from niftynet.layer.resampler import ResamplerLayer
  from tests.niftynet_testcase import NiftyNetTestCase
label_desc: Use image node_id 6 for calling a global function with example usage: get_multiple_3d_targets() and returns: test_target,  test_target