class_docstr: This layer applies a small random (per-iteration) affine
  transformation to an image. The distribution of transformations
  generally results in scaling the image up, with minimal sampling
  outside the original image.
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docstr: create a new layer that will apply the inversed version of
  self._transform. This function write this instance members.
  (calling `self()` after `self.inverse()` might give unexpected results.)
  
  :param interpolation:
  :param boundary:
  :param name:
  :return: a niftynet layer that inverses the transformation of  `self`.
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code: def inverse(self, interpolation=None, boundary=None, name=None):
      """
      create a new layer that will apply the inversed version of
      self._transform. This function write this instance members.
      (calling `self()` after `self.inverse()` might give unexpected results.)
  
      :param interpolation:
      :param boundary:
      :param name:
      :return: a niftynet layer that inverses the transformation of  `self`.
      """
      if interpolation is None:
          interpolation = self.interpolation
      if boundary is None:
          boundary = self.boundary
      if name is None:
          name = self.name + "_inverse"
  
      inverse_layer = AffineAugmentationLayer(
          self.scale,
          interpolation,
          boundary,
          tf.matrix_inverse(self._transform),
          name,
      )
      return inverse_layer
  
file_path: NiftyNet/niftynet/layer/affine_augmentation.py
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.layer.base_layer import Layer
  from niftynet.layer.grid_warper import AffineGridWarperLayer
  from niftynet.layer.layer_util import infer_spatial_rank
  from niftynet.layer.resampler import ResamplerLayer
label_desc: Use image node_id 4 for calling the AffineAugmentationLayer obj's underlying member method code with example usage: obj.inverse(interpolation, boundary, name) and returns: inverse_layer