class_docstr: generate randomised scaling along each dim for data augmentation
code: def randomise(self, spatial_rank=3):
      spatial_rank = int(np.floor(spatial_rank))
      if self.isotropic:
          one_rand_zoom = np.random.uniform(
              low=self._min_percentage, high=self._max_percentage
          )
          rand_zoom = np.repeat(one_rand_zoom, spatial_rank)
      else:
          rand_zoom = np.random.uniform(
              low=self._min_percentage,
              high=self._max_percentage,
              size=(spatial_rank,),
          )
      self._rand_zoom = (rand_zoom + 100.0) / 100.0
  
file_path: NiftyNet/niftynet/layer/rand_spatial_scaling.py
imports: from __future__ import absolute_import, print_function
  import warnings
  import numpy
  import scipy.ndimage
  from niftynet.layer.base_layer import RandomisedLayer
label_desc: Use image node_id 2 for calling the RandomSpatialScalingLayer obj's underlying member method code with example usage: obj.randomise(spatial_rank) without return types