class_docstr: generate randomised elastic deformations
  along each dim for data augmentation
code: def randomise(self, image_dict):
      images = list(image_dict.values())
      equal_shapes = np.all(
          [
              images[0].shape[: self.spatial_rank]
              == image.shape[: self.spatial_rank]
              for image in images
          ]
      )
      if equal_shapes and self.proportion_to_augment >= 0:
          self._randomise_bspline_transformation(images[0].shape)
      else:
          # currently not supported spatial rank for elastic deformation
          # should support classification in the future
          print("randomising elastic deformation FAILED")
          pass
  
file_path: NiftyNet/niftynet/layer/rand_elastic_deform.py
imports: from __future__ import absolute_import, print_function
  import warnings
  import numpy
  from niftynet.layer.base_layer import RandomisedLayer
  from niftynet.utilities.util_import import require_module
label_desc: Use image node_id 2 for calling the RandomElasticDeformationLayer obj's underlying member method code with example usage: obj.randomise(image_dict) without return types