class_docstr: generate randomised scaling along each dim for data augmentation
code: def __init__(
      self,
      min_percentage=-10.0,
      max_percentage=10.0,
      antialiasing=True,
      isotropic=False,
      name="random_spatial_scaling",
  ):
      super(RandomSpatialScalingLayer, self).__init__(name=name)
      assert min_percentage <= max_percentage
      self._min_percentage = max(min_percentage, -99.9)
      self._max_percentage = max_percentage
      self.antialiasing = antialiasing
      self.isotropic = isotropic
      self._rand_zoom = None
  
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 1 to create a new RandomSpatialScalingLayer object from inherited base classes: RandomisedLayer with example: obj = RandomSpatialScalingLayer(min_percentage, max_percentage, antialiasing, isotropic, name)